This article explains what is non functional testing as one of the mission-critical QA procedures, manifests differences between functional and non functional testing techniques, showcases non functional testing perks, dwells on non-functional testing types and criteria, offers examples of non functional testing, and enumerates the major bottlenecks of this type of testing.

What is Non-Functional Testing?

The name speaks for itself. As it is easy to guess, non functional testing means a thorough examination of the solution’s key aspects, such as performance, usability, security, reliability, and overall user experience. Why is it called non-functional if all these characteristics describe the product’s functioning, in fact?

Traditionally, functional tests aim to validate that the software system operates in line with its functional requirements. In other words, to check that it does what it is created to do (perform payments, play a video game, stream content, schedule hospital appointments, book tickets, you name it).

The non functional testing definition doesn’t assess what the software application does. It is honed to ensure the solution does it well, guaranteeing maximum user satisfaction. No matter whether you buy a vehicle insurance online or sell apparel on an e-store, non functional software testing should safeguard the product’s ease of use, responsiveness, fast download, safety, and reliability in different environments and various conditions.

To better illustrate the differences between non-functional and functional testing, let’s juxtapose them in the following table.

While being fundamental for a solution’s adequate operation, non functional testing in software testing is often viewed as an expensive and rather complicated addition to the absolutely necessary functional testing types. However, an efficient usage of non-functional testing examples can usher in numerous benefits.

Assets of Non Functional Testing Dissected

As a company specializing in conducting multiple software tests, we see the following improvements to the application that undergoes non-functional tests during the software development process.

• Enhanced performance. Running various non functional testing examples allows development teams to expose performance-affecting bottlenecks and eliminate them.
• Less time-consuming. Conventionally, non-functional tests take less time than other QA procedures.
• Augmented user experience. Usability testing, as a crucial type of non functional testing, enables software creators to optimize the UI and make the solution exclusively user-friendly.
• Greater security. After conducting certain types of non functional testing, you can reveal the product’s security vulnerabilities and ensure its protection against online threats and cyberattacks from both internal and external sources.

What are non functional testing procedures that can let you enjoy the benefits mentioned above?

Types of Non-Functional Testing: A Comprehensive List

Non functional testing types are categorized into several major classes, each of which relies on specific non functional testing methods.

Performance Testing

Performance testing is non functional testing honed to evaluate a system’s speed, stability, and responsiveness under different conditions, identify performance issues, and eliminate them. Performance tests leverage the following methods.

Load Testing

It assesses the solution’s ability to run under an expected amount of traffic by simulating the activity of multiple users who try to access your site or app simultaneously. Test results display the system’s efficiency in handling the anticipated load. If you subject the product to extreme exploitation conditions and ultra-heavy loads that rarely occur in real-world situations, load testing turns into stress testing, revealing the solution’s limits.

Volume Testing

Also known as flood testing, this data-oriented technique examines how well the system can process large data volumes without worsening its performance. It helps ensure high data throughput and minimize data loss risks.

Endurance Testing

Its alternative name is soak testing. It is intended to evaluate a system’s reliability and stability over extended periods – say, a month – and detect issues (like performance degradation or memory leaks) that may remain unnoticed during shorter QA cycles.

Responsive Testing

This testing technique aims to guarantee a smooth experience of a solution across various devices with different screen parameters. Thanks to it, you can determine design adaptivity when the website or app is opened on a gadget with an unorthodox screen size.

Recovery Testing

During this procedure, testers intentionally break the solution, causing its crashes, network disruptions, or simulating hardware failures to see how well and how quickly it can regain its initial operation while suffering minimal data loss.

Security Testing

Its province is weaknesses and vulnerabilities within the solution that should be eliminated to avoid data breaches and system compromise. Its methods include:

Accountability Testing

This method ensures that the system as a whole or each functionality in particular renders results according to expectations.

Vulnerability Testing

Living up to its name, the testing process here focuses on detecting vulnerabilities and subsequently patching them before they lead to serious security issues.

Penetration Testing

Typically employed by white-hat hackers, this methodology is based on simulating cyber attacks and allows QA teams to identify potential gaps that real-life wrongdoers can exploit and rule out unauthorized access to the system.

Usability Testing

It is conducted from a user’s perspective and aims to clarify how convenient the solution’s usage is and whether it is pleasant to interact with. There are three basic methods within this type of software testing.

Accessibility Testing

The technique is used to verify the product’s compliance with accessibility guidelines (such as WCAG) and make sure it can be used by people with visual, auditory, and locomotive disabilities.

Visual Testing

It aims to reveal visual defects and guarantee that each element on the webpage or application has the intended size, shape, color, and placement.

User Interface Testing

Unlike the previous type, which is honed to assess the conformity of the actual outcome to the initial design concept, UI testing deals with layout aesthetics. The major yardstick here is the visual appeal of the interface.

Other Testing Types

Alongside the strictly categorized types, there exist different methods aimed at ensuring other non-functional requirements of software quality.

Portability Testing

Here, several testing environments are leveraged to check the solution’s operation, allowing testers to determine how well it can transfer from one environment to another. The chief method used to check portability is installation testing, but this type also includes uninstallation, migration, and adaptability testings.

Reliability Testing

This is an umbrella term covering multiple techniques honed to assess the system’s ability to display a consistent and failure-free performance under different conditions. Such techniques encompass regression, failover, continuous operation, redundancy, error detection, and some other testing methods.

Compatibility Testing

Software products never function in isolation but work as part of a larger infrastructure. Compatibility testing that includes cross-browser, cross-platform, software version, driver, hardware, device, and other compatibility checking methods is used to verify that the solution sees eye to eye with various configurations and systems.

Localization Testing

This type of compatibility testing focuses on ensuring the software’s adaptability to a wide range of languages, currencies, measurement units, and other cultural settings.

Scalability Testing

Companies planning to expand can’t do without it, as it evaluates the enterprise software’s potential to increase the number of users and/or simultaneously performed functions.

Compliance Testing

Sometimes considered part of security testing, this method assesses the solution’s adherence to universal and industry-specific regulations and allows its owner to avoid fines and other penalties.

How can I conduct such a heap of tests, you may ask? It is going to take ages to complete them, you may presume. Don’t be scared. Today, the majority of non-functional tests are conducted with the help of AI-powered tools that enable development teams to leverage AI agents in their QA pipeline, thus accelerating the process immensely without compromising on its accuracy and quality.

What software characteristics are checked by all these procedures?

Non-Functional Testing Parameters Exposed

The numerous non-functional testing use cases focus on the following vital criteria of software quality.

1. Security, or how resistant the system is to penetration attempts, and whether it allows data leakages.
2. Reliability, or to what extent the software performs its functions without failures.
3. Survivability, or how well the application recovers if a failure does occur.
4. Availability, or the percentage of the product’s uptime.
5. Accessibility, or what the limitations are for the solution to be used by physically disadvantaged audiences.
6. Efficiency, or how well the system utilizes resources to perform a function. Typically exposed through efficiency testing.
7. Compatibility, or how well the solution dovetails into the ecosystem and plays well with third-party resources.
8. Usability, or whether the product is user-friendly in onboarding and navigating.
9. Flexibility, or how the solution responds to uncertainties while staying fully functional.
10. Scalability, or whether the product can upscale its processing capacity to meet a surge in demand.
11. Reusability, or what assets of the existing system can be leveraged in a new SDLC or another solution.
12. Interoperability, or whether the software can exchange data with its elements or other applications.
13. Portability, or how easily the product can be moved from one ecosystem to another.

As a rule, all these aspects are checked within an all-encompassing procedure consisting of various test types. Here is an example of non functional testing of an imaginary medical solution involving different parameters.

While running different types of non-functional tests, it is essential to bypass roadblocks and bottlenecks along the way.

Non-Functional Testing Challenges and Best Practices

What are the most widespread obstacles QA teams should overcome during a non-functional testing routine?

• The repeated nature of the procedure. Non-functional testing isn’t a one-off effort you have to grind away at and call it a day. It should be conducted regularly, especially after the solution is upgraded, updated, migrated, or modified in any other way.
• Constant changes. Technologies, machines, and users continue to evolve at a breakneck speed. In such a dynamic landscape, it is hard to achieve consistency in test results.
• Complexity. The sheer amount of checks to conduct is staggering, to say nothing of their proper preparation and implementation.
• Broad coverage. You shouldn’t leave any vital software parameter unattended; otherwise, the solution’s overall quality will turn out substandard.
• Time and resources. To perform the entire gamut of non-functional tests and simulate real-world scenarios, you need a lot of workforce, tools, and time.
• Cost. Cutting-edge tools and AI-driven test management software are big-ticket items, so conducting the full scope of non-functional tests is going to cost you a pretty penny.

Evidently, an exhaustive non-functional testing is a no-nonsense endeavor that requires off-the-chart expertise and innovative tools. By addressing Testomat.io, you can receive a competent consultation on performing any kind of software tests and acquire state-of-the-art testing tools that will streamline and facilitate the process to the maximum.

To Draw a Bottomline

Unlike functional testing, which is honed to verify that a software product lives up to the customer’s business and technical requirements, non-functional testing aims to ensure the solution does its job well. The parameters non-functional testing evaluates are a solution’s security, reliability, survivability, accessibility, efficiency, compatibility, usability, scalability, portability, interoperability, and more. All these aspects are checked with non-functional tests of various types, each of which incorporates several techniques.

You can enjoy all the perks non-functional tests provide (excellent performance, improved user experience, exclusive security, etc.) by automating the routine using AI-fueled tools and addressing commonplace challenges within the testing pipeline with the help of the Testomat.io tool.

The post The Basics of Non-Functional Testing appeared first on testomat.io.

This guide will give you clear definitions of white box testing with zero buzzwords, test techniques that scale across QA workflows and advanced use cases like white box penetration testing.

What Is White Box Testing?

White box testing, also known as clear box testing and glass box testing is a software testing technique where the tester has full visibility into the application’s code, structure, logic, and architecture.

What is White Box Testing in Software Engineering?

White box testing definition: soft approach which acts on the internal structure of the software, path, and logic, through reading or executing the source code. The tester (often a Developer, Automation QA Engineer or SDET) looks inside the code to test how well it functions from the inside out, rather than just checking if the system behaves correctly from a user’s point of view. That’s why this technique requires the inside code and control flow and the data flows to be known.

As you can see, white box-test cases navigate across the real execution flows of unit, integration and system testing. They verify edge cases, evaluate conditions, and ensure logical correctness.

Within the software development life cycle (SDLC), white box testing is part of early QA, woven into the development process. It prevents the detection of costly bugs in production in the future.

What You Verify in White Box Testing

White box testing validates multiple layers of software functionality:

• Code Logic and Flow: Every conditional statement, loop iteration, and method execution gets scrutinized. When in your code there is a statement i.e. if-else then with the help of the white box testing you will know that all possible routes are tested and are run properly under proper condition.
• Internal Data Structures: Data structures such as arrays, objects, connection with databases, and memory allocations are checked to verify whether they can process data correctly and with high efficiency.
• Security Mechanisms: Authentication procedures, encryption patterns and access control requests are verified to make sure that make them secure against unauthorized access and data leaking.
• Error Handling: Exception handling, error messages and recovery are exercised to make sure that application handles unexpected situations gracefully.
• Integration Points: The APIs, database connectivities, and third party services integration will be tested to make sure, that they talk with each other and that failures are handled properly.
• Performance Bottlenecks: Analyze the usage of the resources, memory leaks, and execution time to identify bottlenecks in terms of the internal logic of the software where performances are bottlenecked.

White Box Testing vs Other Testing Methods

Understanding the differences between white box, black box, and gray box testing clarifies when each approach provides maximum value:

When White Box Testing Is Preferred

White box testing is preferred when coverage needs deep defect analysis and strict early fault detection. Namely:

• To detect vulnerabilities, source code analysis is needed when security audits are conducted.
• Complicated business logic should undergo validation farther than external behavior
• The compliance regulations dictate that there should be evidence of comprehensive testing of critical systems
• To optimize performance, it is necessary to detect the bottlenecks of algorithms
• Useful after code changes to confirm that internal logic remains intact after regression Testing:
• Teams developers or QA engineers who have access to and an understanding of the source code.

Advantages and Limitations of White Box Testing

Advantages	Limitations
Ensures thorough logic validation through line-by-line code inspection	Requires testers with programming and code analysis skills
Detects bugs early in development (unit/integration testing)	White-box testing is expensive for businesses, so unit or integration testing is not conducted by them typically
Exposes hidden security flaws like hardcoded credentials or weak validation	High maintenance overhead—tests must be updated with code changes
Improves code quality and maintainability	Doesn’t cover user experience flows
Supports automated workflows and CI/CD	Tool-dependent (code coverage, static analysis)
Enables precise test coverage measurement via code analysis	Limited for system-level and third-party testing

Types of White Box Testing

Understanding the different white box testing types helps teams select appropriate white-box testing approaches for specific validation needs. Individual types of white box testing are used to check different areas of the internal structure of the software, so it is possible to conduct thorough quality assurance due to using them strategically.

1⃣ Unit Testing

Unit testing is the lowest level of white-box test, which tests functions, methods, or classes singly. Each such conditional branch, loop iteration and exception handling block is verified with structured white box testing methods in a unit.

Unit tests ensure that every component works as expected under certain inputs, that it gracefully handles edge cases and that it combines with its dependencies. Let us take an example of password validation using white box testing:

python

def validate_password(password):
    """Validates password strength according to security policy"""
    if not password:                           # Path 1: Empty password
        return False, "Password required"
   
    if len(password) < 8:                      # Path 2: Too short
        return False, "Password must be at least 8 characters"
   
    has_upper = any(c.isupper() for c in password)     # Path 3a: Check uppercase
    has_lower = any(c.islower() for c in password)     # Path 3b: Check lowercase
    has_digit = any(c.isdigit() for c in password)     # Path 3c: Check numbers
    has_special = any(c in "!@#$%^&*" for c in password)  # Path 3d: Check special chars
   
    if not (has_upper and has_lower and has_digit and has_special):  # Path 4
        return False, "Password must contain uppercase, lowercase, number, and special character"
   
    return True, "Password valid"              # Path 5: Success

White box unit testing for this function requires test cases covering all execution paths, validating both successful and failed validation scenarios.

2⃣ Integration Testing

The white box test used as integration testing ensures that the interaction among the various components of software is valid. In contrast to black box integration testing which only looks at how the interfaces behave, white-box testing looks into the real data flow between components, the calls to the methods and the shared resources.

This example of white box testing presents the scenario of testing a user registration system in which several elements are combined:

Python

class UserRegistrationService:
    def __init__(self, db_service, email_service, password_encoder):
        self.db_service = db_service
        self.email_service = email_service
        self.encoder = password_encoder

    def register_user(self, user_data):
        # Path 1: Validate input data
        if not self._is_valid_user_data(user_data):
            return RegistrationResult(False, "Invalid user data")

        # Path 2: Check if user exists
        if self.db_service.user_exists(user_data.email):
            return RegistrationResult(False, "User already exists")

        # Path 3: Encode password and save user
        encoded_password = self.encoder.encode(user_data.password)
        new_user = self.db_service.save_user(user_data, encoded_password)

        # Path 4: Send welcome email
        self.email_service.send_welcome_email(new_user.email, new_user.name)

        return RegistrationResult(True, "Registration successful")

    def _is_valid_user_data(self, user_data):
        # Example simple validation
        return bool(user_data.email and user_data.password and user_data.name)


class RegistrationResult:
    def __init__(self, success, message):
        self.success = success
        self.message = message

White-box integration testing validates that password encoding works correctly, database transactions complete successfully, and email service integration handles failures gracefully.

3⃣ Security Testing

White box security testing (sometimes known as white box penetration testing) probes the source code with white box testing methods in search of security vulnerabilities. Authentication system, encryption algorithms, input validation procedures, and access controls are examined by testers.

This method can find the vulnerabilities that are not detected by external penetration testing, hardcoded passwords, weak cryptographic algorithms, poor input filtering, and privilege escalation. The following is an example of white box testing where a well known security vulnerability has been discovered:

python

# Vulnerable code example
def authenticate_admin(username, password):
    # SECURITY FLAW: Hardcoded admin credentials
    if username == "admin" and password == "defaultPass123":
        return True, "admin"
   
    # SECURITY FLAW: SQL injection vulnerability
    query = f"SELECT * FROM users WHERE username='{username}' AND password='{password}'"
    result = database.execute(query)
   
    if result:
        return True, result[0]['role']
    return False, None

White box security testing immediately identifies these vulnerabilities through source code analysis, enabling targeted remediation before deployment.

4⃣ Mutation Testing

Mutation testing introduces small changes (mutations) to source code to verify that existing test cases can detect these modifications. If tests pass despite code mutations, it indicates gaps in test coverage or ineffective test cases.

This white box testing technique validates the quality of your existing white-box testing suite by ensuring tests can catch actual code defects. Consider this example:

python

# Original function
def calculate_tax(income, tax_rate):
    if income <= 0:
        return 0
    return income * tax_rate

# Mutation 1: Change <= to <
def calculate_tax_mutant1(income, tax_rate):
    if income < 0:  # Mutation: <= changed to <
        return 0
    return income * tax_rate

# Mutation 2: Change * to +
def calculate_tax_mutant2(income, tax_rate):
    if income <= 0:
        return 0
    return income + tax_rate  # Mutation: * changed to +

Effective unit tests should fail when testing these mutations, confirming that the test suite can detect logic errors.

5⃣ Regression Testing

White box regression testing is where modification of existing code does not disrupt the current functionality, through the internal code paths and logic structures are re-tested with well-established white box re-testing methods. This is important especially when modifying complicated algorithms or changing the security solutions. White box tests concerning regression cases are of the following types:

• Code Path Validation: Making sure after refactor functions have the same path of execution
• Algorithm Verification: Verificatory of ensuring that optimized algorithms output accurate results that are the same.
• Integration Point Testing: Ensuring that nobody messes with the interfaces such that a change in communication between components fails
• Performance Regression: Employing white-box testing in order to discover performance deteriorations in certain lines of the code

This is a full-scale way of working out white-box testing thus the software should be of good quality and reliable enough throughout the course of the development since it detects the problems that could have been overlooked by the functional type of testing.

Tools Used in White Box Testing

White Box Testing Techniques

White box testing presents the best methods of ensuring quality application of proper testing in software system. These established practices explore the internal mechanisms of software in a systematic way which ascertains the quality of the software with intensive exploration of the structure and logic of codes. Learning these methods, the teams will be able to adopt the best practices, which can meet design documents and organizational standards.

Code Coverage Analysis

Code coverage analysis is the capacity to gauge the portion of your coding that is actually called during testing and is a primary software test method of determining the performance of tests applied. The various namings offer varied degrees of knowledge of how the software works internally:

Statement Coverage Statement coverage measures the percentage of executable statements that tests execute during the software testing process. This basic metric provides initial visibility into which parts of the code structure receive validation. If your code contains 100 statements and tests execute 85 of them, you achieve 85% statement coverage.

python

def calculate_discount(price, customer_type):
    discount = 0                    # Statement 1
    if customer_type == "premium":  # Statement 2 - Decision point
        discount = 0.2              # Statement 3
    elif customer_type == "regular": # Statement 4 - Decision point
        discount = 0.1              # Statement 5
    else:                           # Statement 6 - Decision point
        discount = 0                # Statement 7
   
    return price * (1 - discount)   # Statement 8

Achieving 100% statement coverage requires test cases for premium customers, regular customers, and unknown customer types. Although, statement coverage does not identify logical errors in decision logic because a test case exercising the premium path will provide a partial coverage, but will fail to check on the other customers.

Branch Coverage Branch coverage checks that all decision points (if-else statement, switch statements) are executed through correct paths, namely, through both true and false branches, and such thorough examination of the internal execution of a software is in greater depth than statement coverage. Higher branch coverage typically indicates more thorough testing and better adherence to best practices in quality assurance.

Consider this enhanced example showing branch coverage analysis:

python

def process_loan_application(credit_score, income, loan_amount):
    if credit_score >= 700:        # Branch 1: True/False paths
        if income >= loan_amount * 3:  # Branch 2: True/False paths
            return "Approved"
        else:
            return "Approved with conditions"
    else:
        if income >= loan_amount * 5:  # Branch 3: True/False paths
            return "Manual review required"
        else:
            return "Denied"

Complete branch coverage requires test cases ensuring each conditional statement evaluates to both true and false, revealing logical errors that statement coverage might miss.

Path Coverage Path coverage looks at all the possible paths through the structural code in the program and is therefore the most thorough method of software testing complex logic. This makes way to many test cases, since this method is not suitable in functions that have many conditional branches. To achieve path coverage in the loan application functionality above, it is necessary to have four test cases:

1. High credit score (≥700) + Sufficient income (≥loan_amount * 3)
2. High credit score (≥700) + Insufficient income (<loan_amount * 3)
3. Low credit score (<700) + High income (≥loan_amount * 5)
4. Low credit score (<700) + Low income (<loan_amount * 5)

Condition coverage checks that boolean expressions are true and false. In complicated situations involving many operators, this software testing method will make sure that each one is tested separately by following the best practices of thorough quality assurance insurance.

Control Flow Testing

Control flow testing is used to verify the logical integrity of the programs through the analysis of program flows that direct the progress of execution along various code paths in the inner functions of the software. The software testing approach places every possible route over the code structure and forms test cases to those paths and makes them compatible with design documents and specifications.
As an example, suppose you have a function that has nested conditions: in this case control flow testing will be used so that all conditions combinations are tested, not just the happy path. This will uncover logical erroneousness that a simple form of testing may be unable to notice:

python

def validate_user_access(user_role, resource_type, time_of_day):
    if user_role == "admin":               # Control flow path 1
        return True
    elif user_role == "manager":           # Control flow path 2
        if resource_type == "reports":     # Nested control flow 2a
            return True
        elif resource_type == "data":      # Nested control flow 2b
            return 9 <= time_of_day <= 17  # Business hours only
    elif user_role == "user":              # Control flow path 3
        if resource_type == "public":      # Nested control flow 3a
            return True
   
    return False                           # Default control flow path

Systematic control flow testing ensures each execution path gets validated according to best practices in the software testing process.

Data Flow Testing

Data flow testing is a method of software testing, which follows the flow of the data among variables, parameters and data structures and is an invaluable piece of software testing to detect logic errors in the internals of the software. This method of quality assurance fits in naturally with the static code analysis.

python

def calculate_employee_bonus(employee_data):
    base_salary = employee_data.get('salary')  # Data definition
    performance_rating = employee_data.get('rating')  # Data definition
   
    if base_salary is None:  # Data usage - undefined check
        return 0
   
    bonus_rate = 0  # Data definition
    if performance_rating >= 4.0:  # Data usage
        bonus_rate = 0.15  # Data redefinition
    elif performance_rating >= 3.0:  # Data usage
        bonus_rate = 0.10  # Data redefinition
   
    total_bonus = base_salary * bonus_rate  # Data usage
    return total_bonus  # Data usage

Data flow testing validates that each variable follows proper definition-usage patterns throughout the code structure.

Loop Testing

Loop testing validates different loop scenarios within the software’s inner workings, ensuring that iterative code structure elements behave correctly under various conditions. This software testing technique represents essential best practices for comprehensive quality assurance during the software testing process.

Loop testing addresses several critical scenarios:

Simple Loop Testing

• Zero Iterations: Ensures loop handles empty collections gracefully
• One Iteration: Validates single-pass execution logic
• Typical Iterations: Tests normal operational scenarios (2 to n-1 iterations)
• Maximum Iterations: Confirms boundary condition handling

python

def process_transaction_batch(transactions):
    processed_count = 0
    failed_transactions = []
   
    for transaction in transactions:  # Simple loop requiring loop testing
        try:
            if validate_transaction(transaction):
                execute_transaction(transaction)
                processed_count += 1
            else:
                failed_transactions.append(transaction.id)
        except Exception as e:
            failed_transactions.append(transaction.id)
   
    return processed_count, failed_transactions

Nested Loop Testing Loop testing for nested structures requires systematic validation of inner and outer loop interactions:

python

def analyze_sales_data(regions, months):
    results = {}
   
    for region in regions:        # Outer loop
        region_totals = []
        for month in months:      # Inner loop - nested loop testing required
            monthly_sales = calculate_monthly_sales(region, month)
            region_totals.append(monthly_sales)
        results[region] = sum(region_totals)
   
    return results

Concatenated Loop Testing Sequential loops require loop testing to ensure data flows correctly between loop structures:

python

def optimize_inventory(products):
    # First loop: Calculate reorder points
    reorder_needed = []
    for product in products:
        if product.current_stock < product.minimum_threshold:
            reorder_needed.append(product)
   
    # Second loop: Generate purchase orders (concatenated loop testing)
    purchase_orders = []
    for product in reorder_needed:
        order = create_purchase_order(product)
        purchase_orders.append(order)
   
    return purchase_orders

Static Code Analysis Integration Modern loop testing leverages static code analysis tools to identify potential issues before execution:

• Infinite Loop Detection: Identifies loops lacking proper termination conditions
• Performance Analysis: Highlights loops with excessive complexity
• Memory Usage Patterns: Detects loops that might cause memory exhaustion

These comprehensive white box testing techniques ensure that the software testing process validates every aspect of the software’s inner workings, maintaining software quality through systematic application of proven quality assurance methodologies. Following these best practices helps teams catch logical errors early while ensuring their implementations match design documents and architectural specifications.

Example of White Box Testing in Practice

Let’s examine a practical white box testing example using a simple authentication function:

python

def authenticate_user(username, password, max_attempts=3):
    """
    Authenticate user with username and password
    Returns: (success: bool, message: str)
    """
    if not username or not password:           # Path 1
        return False, "Username and password required"
   
    if len(password) < 8:                      # Path 2
        return False, "Password too short"
   
    # Check if account is locked
    attempts = get_failed_attempts(username)    # Path 3
    if attempts >= max_attempts:               # Path 4
        return False, "Account locked"
   
    # Verify credentials
    if verify_password(username, password):    # Path 5
        clear_failed_attempts(username)        # Path 6a
        return True, "Login successful"
    else:
        increment_failed_attempts(username)    # Path 6b
        remaining = max_attempts - attempts - 1
        if remaining > 0:                      # Path 7a
            return False, f"Invalid credentials. {remaining} attempts remaining"
        else:                                  # Path 7b
            lock_account(username)
            return False, "Account locked due to failed attempts"

White Box Test Cases

Based on the code structure, comprehensive white box test cases include:

Test Case 1: Empty Username (Path 1)

python

def test_empty_username():
    result, message = authenticate_user("", "password123")
    assert result == False
    assert message == "Username and password required"

Test Case 2: Short Password (Path 2)

python

def test_short_password():
    result, message = authenticate_user("john", "123")
    assert result == False
    assert message == "Password too short"

Test Case 3: Account Already Locked (Path 4)

python

def test_locked_account():
    # Setup: Account has 3 failed attempts
    set_failed_attempts("john", 3)
    result, message = authenticate_user("john", "password123")
    assert result == False
    assert message == "Account locked"

This example demonstrates how white box testing validates every execution path, ensuring the authentication logic handles all scenarios correctly.

White Box Penetration Testing (Advanced Use Case)

White box penetration testing or white box pen testing is a more sophisticated method of security assessment in which the penetration testers have ready access to source code, design documentation and architectural knowledge of the system.

What is White Box Pen Testing?

White box pen testing is the scenario of insider threat by using the inside knowledge of the system. As compared to the black box penetration testing where the external attackers have no knowledge of the application and maliciously penetrate it, the white box pen test supposes that the attackers are familiar with the inner structure of the application. This strategy is always priceless in:

• Source Code Security Reviews: Identifying vulnerabilities in authentication mechanisms, encryption implementations, and access controls.
• Architecture Analysis: Finding security flaws in system design and component interactions.
• Configuration Audits: Validating that security settings match organizational policies.
• Compliance Validation: Demonstrating thorough security testing for regulatory requirements.

Common Myths About White Box Testing

Myth 1: “White box testing eliminates the need for other testing types”

Fact: White box testing is supplementary to rather than a substitute of black box testing, system testing and user acceptance testing. The two approaches certify various parameters of software quality.

Myth 2: “100% code coverage guarantees bug-free software”

Reality: Code coverage does not measure effectiveness of tests; it measures completeness of the tests. Poor test cases may give one 100 percent coverage but may not cover edge cases and errors in business logic.

Myth 3: “White box testing is only for developers”

Fact: Of course, knowledge of programming is useful, but it is possible to train specifically QA as a specialist to perform white box testing, and their testing ideas can fill gaps in developer testing.

Myth 4: “Automated tools handle all white box testing needs”

Reality: Analysis and coverage tools are helpful metrics to be considered, although the judgment of human insight is required to specify relevant test cases and explain the outcomes.

Myth 5: “White box testing is too expensive for small projects”

Fact: Built-in testing and coverage are provided by the modern IDEs, and white box testing is no longer inaccessible (because of the open-source frameworks) no matter the size of a project.

When to Use White Box Testing

White box testing can be maximized by strategic implementation, at controlled expense of defending the costs and complexity:

During Unit and Integration Phases

White box testing is most useful in an initial development stage when code access is common and change costs are more affordable:

• Unit Development: Ensure that functions, methods and classes are correct as developers code them.
• Integration Development: maintain the interaction of components with properly defined interfaces.
• Refactoring: Make sure that functionality is not destroyed by the changing code.

For Security Audits with Source Code Access

White box security testing is advantageous to organizations that possesses internal development or security orienting needs:

• Financial Services: Demonstrating rigor when it comes to the security testing may also be necessary in order to comply with regulation.
• Medical Applications: The security of source code can be validated as a HIPAA compliant application in healthcare applications.
• Government Contracts: The need to have security clearance could demand white box security tests.

In Test-Driven Development

TDD has naturally included the concepts of white box testing because it demands testing even prior to implementation:

• Red-Green-Refactor Cycle: Write the failing tests, apply the code that passes the tests, refactor, and repeat it, keeping the test coverage intact.
• Behavior-Driven Development: Apply white box techniques to confirm that behavior specified for implementation is achieved.

In Performance Optimization

White box testing can find bottlenecks in performance that cannot be found using external testing:

• Analysis of Algorithms: Analyse multi-complex calculations, sorting algorithms, and data processing algorithms
• Memory Management: detect memory leaks, over allocations, and cleanup problems of the resources
• Concurrency Testing: Corroborate the thread safety, deadlock aversion and management of contending resources

Conclusion

White box testing gives you deep insight into application’s code, surfaces hidden logic bugs, ensures thorough test coverage, and supports early defect detection. It’s not a standalone solution, but a vital part of a modern QA strategy, especially when powered by tools like Testomat.io, which brings automation, AI agents, and cross‑team collaboration into the same workspace.

The post White Box Testing: Definition, Techniques & Use Cases appeared first on testomat.io.

However, if you are an entrepreneur owning an internet-driven business, a poor system’s ability to respond to users’ commands is not just an awkward nuisance. It will cost you a pretty penny since 53% of people will abandon the site if it takes over three seconds to load. If you launch an e-commerce site, ensure it can handle a large number of users on special occasions or during holidays.

The only recipe for eliminating performance issues and preventing performance degradation is conducting an out-and-out performance testing.

This article explains the essence of performance testing, outlines its use cases, explores various types of performance testing with examples, helps choose among different types of performance testing, provides a roadmap for conducting performance tests, suggests a list of test automation tools, and highlights common mistakes made by greenhorns during the procedure.

Performance Testing Made In a Nutshell

Performance testing is the key non-functional software testing method that aims to expose the solution’s responsiveness, stability, scalability, and speed under various network conditions, data volumes, and user loads.

When properly planned and implemented, performance tests allow software creators and owners to:

• Expose performance bottlenecks
• Identify potential issues and points of failure
• Minimize downtime risks
• Ensure meeting latency and load time benchmarks
• Optimize system performance under heavy user traffic
• Improve user experience
• Enhance the solution’s scalability
• Evaluate how the system handles recovery
• Validate the reliability, stability, and security of the software piece in different scenarios (like peak traffic, DDoS attack, or sustained usage) and across different environments

Performance testing comes as a natural QA routine at the end of the software development process. Yet, there are certain events during and after the SDLC that make performance testing vital.

When Should You Conduct Performance Testing?

The necessity of conducting particular performance testing types is conditioned by the solution you are building. For example, if you are crafting a gaming app, you should run various types of software performance testing to test data, databases, servers, and networks and verify that it works well on devices with different screen sizes, renders visuals properly, and tackles multiplayer interactions between concurrent users seamlessly.

Typically, various performance testing types in software testing of a product are conducted continuously if you stick to the Agile development methodology, and at least once in case you employ the waterfall approach to SDLC. Besides, multiple software performance testing types are advisable:,

• at early development stages to identify possible issues
• after adding new features
• after significant updates
• prior to major releases
• before the anticipated traffic spikes or user quantity expansion
• regularly in environments identical to the production environment

The best practices of performance testing presuppose conducting checks in the automated mode. Which types of tests are used in automated performance testing?

Dissecting the Types of Performance Testing in Software Testing

Different types of testing in performance testing are honed to check various aspects of the solution’s functioning. Here is a roster of the types of performance testing with examples.

1. Load Testing

It assesses the system’s ability to handle an unusual amount of traffic or user interactions without slowing down or – God forbid – crashing. Load tests allow you to gain scalability insights, minimize the solution’s downtime, mitigate data loss risks, and consequently save time and money.

Example: Simulate 10,000 virtual users browsing your e-store and simultaneously adding goods to carts.

2. Stress Testing

Unlike load tests, which deal with expected loads during normal usage, a stress test aims to detect the system’s limits by subjecting it to extreme conditions. When properly performed, this type of performance test exposes the solution’s weak points, enhances its stress reliability, and ensures regulatory compliance.

Example: Apply a sudden surge of 20,000 shoppers at a flash sale event.

3. Scalability Testing

This technique assesses the product’s capability to cope with the gradually increasing number of users and/or transactions. Scalability tests showcase the system’s growth potential, reduce OPEX, and augment user experience.

Example: Add virtual users incrementally to understand response times and conduct server load capacity testing.

4. Endurance Testing

Also called soak testing, this technique enables QA teams to detect memory leaks and performance degradation when the system operates over an extended period. Thanks to soak tests, you can reveal issues that may not manifest themselves during shorter load or stress testing procedures.

Example:Run a fintech app for a month under sustained usage to assess its long-term stability and basic performance metrics.

5. Spike Testing

Both stress testing and spike performance testing types simulate sudden increases in traffic. Yet the aim of spike tests is different. They reveal how the system handles and especially recovers from traffic surges related to promotional events, viral social media campaigns, or product launches. Spike tests forestall system-wide failures, optimize resource utilization, and enhance the solution’s uptime.

Example: Simulate a 5x or 10x traffic surge on Black Friday or Cyber Monday for an e-commerce platform.

6. Volume Testing

Among other types of performance testing, this one is data-oriented, making it crucial for databases and data-driven software. It is intended to ensure the system remains functional and swift in performance even if a large amount of data is ingested. Volume testing is honed to minimize data loss risks during memory usage and guarantee high data throughput.

Example: Check the system’s performance and query execution times when importing millions of records into its database.

7. Peak Testing

Its overarching goal is to identify the maximum load a system can be subject to and understand what happens if this threshold is crossed. Peak tests help determine the solution’s maximum capacity and minimize the possibility of crashes.

Example: Monitor an e-store’s throughput and response time when a maximum number of virtual users simultaneously browse it, add items to the cart, and pay for the purchase.

8. Resilience Testing

This technique is honed to assess a system’s capability to withstand disruptions and resume its normal functioning after one occurs. It helps QA personnel identify single points of failure and proactively eliminate them, thus mitigating downtime risks and improving disaster recovery.

Example: For a fintech platform, simulate a shutdown of a database server during a money transfer or other transaction to see if users face service interruption, the data remains safe, and the system bounces back fast.

9. Breakpoint Testing

The method aims to identify the breaking point – a moment when the system fails. It allows you to determine the conditions under which the solution becomes unresponsive or unstable, thus enhancing capacity planning and preventing downtime in advance.

Example: Gradually increase the number of a video streaming platform’s concurrent users to detect the figure after which the video streaming quality plummets dramatically or when buffering becomes excessive.

Now that you know which types of tests are used in automated performance testing, you may wonder how you can select the best technique.

Choosing the Proper Types of Performance Tests

Here is the checklist that you should stick to while opting for certain test types to apply.

• Identify your solution’s critical needs. Do you prioritize handling heavy loads, go for rapid traffic spikes, build for resilience, or deal with surges in data volumes?
• Analyze the user base. Do you expect it to grow significantly, or will it remain relatively stable?
• Assess system failure risks. Do you expect it to run continuously (like gaming apps or streaming platforms) or does its functioning involve a sequence of distinct steps?
• Consider different scenarios. What are the solution’s typical use cases, and what are their possible implications?
• Envisage continuous adjustment and monitoring. Different types of performance testing aren’t one-time efforts. Consider them as elements of a comprehensive testing strategy that should be revisited once a significant overhaul of the solution occurs.
• Apply the right tools. Each specific tool serves a certain purpose that should align with the system’s vital parameters.

Performance Test Automation Tools: A Detailed Comparison

Let’s have a look at specialized software that streamlines and facilitates performance testing.

Today, AI-driven testing tools (like Testomat.io, Functionize, Mabl, or Dynatrace) are making a robust entry into the high-tech realm, enabling testing teams to essentially accelerate test generation and execution. However, it is humans who are ultimately responsible for running all the types of performance testing.

How to Conduct Performance Testing: A Step-by-Step Guide

Specializing in providing testing services, Optimum Solutions Sp. z o.o. recommends you to adhere to the following straightforward performance testing roadmap.

Step 1. Start With Planning

Draw a detailed testing strategy that contains answers to such questions as what aspects you are going to test, what are the different types of performance testing techniques to be used in the process, what key metrics you will leverage to measure the results, what tools will dovetail with the testing’s scope and objectives, and others.

Step 2. Set Up the Environment

The test should reflect the production environment as much as possible, and test inputs should resemble those your solution will deal with in the real world. Besides, you should have tracking tools in place to monitor test execution and results.

Step 3. Run the Test

You should start small and gradually increase the load, keeping an eye on performance indices (response time, error rate, etc.). All results should be captured by the detailed log for a deeper post-test analysis.

Step 4. Analyze the Results

Such an analysis involves comparing the received metrics against the expected benchmarks, detecting trends indicative of issues, exposing the root causes of failures, and identifying areas for improvement.

Step 5. Optimize and Reiterate

After understanding the reasons for problems, introduce remedial changes (optimize code, upgrade resources, tweak configurations, etc.). Then, re-run the test to make sure the adjustments you implemented had a positive impact on the targeted performance aspects.

While conducting any of the aforementioned types of performance testing, you should watch out for typical mistakes.

Common Performance Testing Mistakes to Avoid

Inexperienced testers very often overlook certain details that can ultimately ruin the entire process. What are they?

• Underestimating the cost. Meticulous recreation of production conditions is not a chump change issue, so you should have sufficient funding for performance testing.
• Lack of planning. Crafting user case scenarios and load patterns requires a thorough preliminary planning, without which the success of the procedure is dubious.
• Disregarding data quality. Inconsistent and irrelevant data leveraged for testing can distort the results.
• Technological mismatch. Testing tools should play well with a particular tech stack; otherwise, you will waste a lot of time trying to align them.
• Starting to test late in the SDLC. You can launch tests as soon as a unit is built. Relegating performance testing to later stages increases the number of errors and glitches that need to be corrected.
• Disregarding scalability. You should always take thought for future-proofing and envision the potential growth of the system’s user audience.
• Creating unrealistic tests. Tests you run should simulate the real-world usage scenarios as much as possible and involve operating systems, devices, and browsers with which the solution will work.
• Forgetting about the user. You should always adopt a user’s perspective while gauging the importance of various performance metrics.

As you can easily figure out, preparing and conducting an efficient performance test is a challenging task that should be entrusted to high-profile specialists in the domain, wielding first-rate testing tools. Contact Testomat.io and schedule a consultation with our experts.

Key Takeaways

Performance testing is an umbrella term that encompasses a range of techniques used to verify whether a software piece meets basic performance indices (response time, latency, throughput, stability, resource utilization, and more). It should be conducted after reaching each milestone in the SDLC and in several post-launch situations, such as after adding new features, together with introducing updates, and before anticipated surges in the number of users or queries.

What are the types of performance testing? Here belong load, stress, scalability, endurance (aka soak), spike, volume, peak, resilience, and breakpoint methods. To obtain good testing results and receive a high-performing solution as a deliverable, you should select the proper test type, opt for the relevant automation tool, follow a straightforward testing algorithm, avoid the most frequent mistakes, and hire competent vendors to assist you with planning and conducting the procedure.

The post All You Need to Know about the Types of Performance Testing appeared first on testomat.io.

This guide breaks down the most frequently asked BDD interview questions into four core categories: BDD fundamentals, Gherkin syntax, automation techniques, and real-world application.

Core Understanding BDD conception

1. What is BDD and why is it important in software development?

Behavior-Driven Development (BDD) is a software development approach that focuses on collaboration between developers, testers, and business stakeholders. By breaking down expected system actions into Given Then When steps, teams create a living specification that’s accessible to everyone, even non-tech teammates. This shared format not only drives implementation but also naturally encourages effectiveness, reduces misunderstandings, and keeps development aligned with real-world needs.

The purpose of the BDD approach is to align technical execution with business goals using a simple language representation of the application behavior. It bridges the gap between technical teams and non-technical stakeholders.

Compared to traditional software testing, BDD enables early detection of bugs, simplifies acceptance criteria, and provides better traceability from requirements to code implementation.

2. Can you explain the core principles of BDD?

The key principles include:

• Collaboration first: Encouraging open communication between all team members.
• Specification by example: Using real-world scenarios to define system behavior.
• Executable specifications: Turning those examples into automated tests.
• Living documentation: BDD scenarios serve as always-up-to-date documentation.
• Outside-in development: Focusing on user needs first, then progressing to technical layers.

3. What distinguishes BDD from traditional testing methodologies?

BDD begins before a single line is written. It brings business analysts, developers, testers, and even stakeholders into a shared conversation – using simple  representations of Given Then When the application behavior to define what the software should do from the user’s perspective.

– But how does BDD truly differ? The table below lays out the key distinctions:

4. How does BDD integrate with Agile methodologies?

BDD and Agile go hand-in-hand. In Agile, requirements evolve through user stories, and BDD supports this by:

• Enabling early and continuous collaboration
• Allowing iterative feedback loops
• Ensuring that acceptance criteria become executable tests

BDD scenarios can become the acceptance criteria in Agile sprint planning and are often automated using a tool like Cucumber

5. BDD in multi-disciplinary development teams: How does BDD enhance communication?

Using simple language (via Gherkin syntax) helps:

• Eliminate misinterpretation of requirements
• Get earlier feedback from stakeholders
• Empower testers to write meaningful cucumber test scenarios for their future automation by developers.

For example, BDD scenario:

Feature: Account Balance

  Scenario: Newly created account should have zero balance
    Given a new account is created
    Then the account balance should be 0

6. What is BDD and how is it different from TDD (Test-Driven Development)?

Unlike traditional unit testing or integration testing, which focus on implementation, BDD begins with the behavior.

Gherkin Language & Feature Files

6. Describe the ‘Given, When, Then’ pattern and its role in BDD

The Given-When-Then syntax defines the structure of BDD scenarios.

• Given: Setup the initial state
• When: Perform an action
• Then: Assert the expected outcome

This mirrors how users think and behaves like a functional test, which increases business involvement and trust in the development process.

7. What is Gherkin?

Gherkin is the programming language used to write BDD scenarios. It is a domain-specific language that follows own indentation and keywords.

Gherkin language example:

Feature: Login Page Authentication

  Scenario: Valid user logs in
    Given the user is on the login page
    When the user enters valid credentials
    Then they should be redirected to the dashboard

It acts as a simple language representation of application behavior, making it accessible to everyone, even those without technical knowledge.

8. What is a Feature File and its structure?

A feature file:

• Is written in .feature format
• Describes a single feature or functionality
• Might contains multiple scenarios
• Can include tags for filtering
• Often starts with a background keyword (optional)

Typical Example structure feature file in Gherkin:

Feature: User Login

  Background:
    Given the user has an account

  @positive
  Scenario: Successful login
    Given the user is on the login page
    When they enter correct credentials
    Then they should see the dashboard

9. What’s the difference between Scenario and Scenario Outline in Gherkin?

Example of Scenario Outline:

Scenario Outline: Login attempts

  Given the user is on the login page
  When the user logs in with <username> and <password>
  Then they should see <result>

  Examples:
    | username | password | result        |
    | john     | 1234     | dashboard     |
    | jane     | wrong    | error message |

It’s ideal when testing many cases with different input data, without duplicating scenarios.

10. How do you write effective BDD scenarios?

Tips to impress in interviews:

• Use clear language
• Only one assertion per scenario
• Avoid writing test implementation details
• Tag properly for filtering using Cucumber options
• Reuse step definitions where possible

Example of a bad scenario:

When I enter "username"
Then I see the page

Better version:

When I enter a valid username and password
Then I should be logged in and redirected to the dashboard

This improves test harness stability and collaboration clarity.

BDD Automation

Behavior-Driven Development (BDD) does not end at writing scenarios in Gherkin, it comes alive when automation enters the picture. Automating BDD scenarios transforms plain-text behavior descriptions into executable tests that verify your application in real-time, ensuring both development and business requirements are aligned.

This section dives deep into how automation in BDD works, how it maps to real code, and how to set up a maintainable and scalable test framework around it.

11. What is the role of step definitions in BDD automation?

Step definitions serve as the crucial bridge between human-readable feature files and actual code implementation. A step definition file contains the code that executes when a particular step in your Gherkin scenario is run.

Example Steps in Gherkin scenario:

Scenario: Successful login
  Given the user is on the login page
  When they enter valid credentials
  Then they should be redirected to the dashboard

The step definition uses a regular expression to match the plain language step. This is where technical knowledge meets simple representation of the application behavior.

Purpose of the steps to automate in .feature file:

• Translate behavior into test actions.
• Keep test logic separated from scenario descriptions.
• Enable software testing teams to reuse steps across multiple scenarios.

12. How do you map Gherkin steps to automation code?

Mapping is handled by matching the steps in .feature files to methods in your step definition file. This is made possible using regular expressions or Cucumber-style expressions.

Example:

Definition in Gherkin:

Given the user is logged in

Could map to Python test expression:

@given('the user is logged in')
def step_impl(context):
    context.browser.get('/login')
    context.browser.fill('username', 'admin')
    context.browser.fill('password', '1234')
    context.browser.click('Login')

Using regular expressions in step definitions allows for flexible matching. This is crucial when you want to support a large number of scenarios with minimal code repetition.

Mapping Gherkin to code is about linking human-readable stories to the underlying test harness that runs your functional testing suite.

13. What tools are essential for implementing BDD, and why?

Several testing tools support BDD workflows, but the most popular is undoubtedly the Cucumber tool.

Tool	Purpose
	Most widely used BDD test framework. Supports Java, JavaScript, Ruby, etc.
	A Python-based BDD tool.
	Lightweight alternative by ThoughtWorks with Markdown syntax. Note that Gauge does not enforce Gherkin syntax Given Then When
	Early Java BDD framework, an alternative to Cucumber.

For successful BDD development, you also need:

• A development framework (Spring, Angular, React, Django, Flask) for initial creation of app logic
• A test automation framework and browser automation library like Selenium, Playwright, or Cypress
• A CI pipeline to run and deployment tests

Cucumber web test cases, when executed as part of your CI\CD pipeline, help ensure you’re building the right features the right way.

14. How do you deal with flaky or redundant BDD scenarios?

BDD scenarios are meant to be reliable documentation and automated tests–but they can become flaky if written without discipline.

• Common Causes of Flakiness:
• UI instability (e.g. dynamic elements on the login page).
• Hardcoded data.
• Poor use of waits.
• Too much dependency between tests.
• Lack of clear ownership or review.
• Misunderstanding of how steps map to automation code.
• Steps that rely on dynamic content without clear selectors or assertions.
• Poorly scoped or reused (dependent) steps across different scenarios.

Strategies to Fix:

• Use Background Blocks Smartly. The background keyword lets you define common preconditions.

Background:
  Given the user is logged in

• But overusing it can cause shared-state problems. Keep it minimal.
• Avoid Duplicates. Many teams write multiple scenarios that test the same thing. Review the purpose of the scenario–does it bring new business value?
• Isolate Tests. Avoid side effects between tests. Reset the database or use stubs/mocks.
• Tag Flaky Tests With cucumber options, so then you can manually isolate unstable tests for later review, for instance --tags @flaky

15. Explain the process of automating tests in a BDD framework

Step 1: Write the Feature

Feature: User Login

Scenario: Successful login
  Given the user is on the login page
  When the user enters valid credentials
  Then they should see the dashboard

Step 2: Hook into the Test Automation Framework

Use JUnit, TestNG, or Playwright testing tools. This setup acts as your test harness. BDD workflow supports unit testing, integration testing, and functional testing all under one readable, maintainable format.

Step 3: Define Steps

Each step is mapped with a test definition file in Java

@Given("the user is on the login page")
public void goToLoginPage() {
    driver.get("https://example.com/login");
}

Step 4: Integrate into CI\CD

Push code to run tests in pipelines, and control failed builds on regression.

Step 4: Run your tests

Execute your test scope and track its readiness for software delivery to the market with Report and Analytics.

16. How to manage test data in BDD scenarios?

Managing test data in BDD (Behavior-Driven Development) scenarios is essential for ensuring clarity, maintainability, and reusability. Here are best practices and strategies to effectively manage test data in BDD:

1. Use Data Tables in Gherkin

Use Gherkin tables to define structured input directly in scenarios:

Given the following users exist:

  | Name     | Email            | Status  |
  | Alice    | alice@test.com   | active  |
  | Bob      | bob@test.com     | inactive |

Makes data visible, readable, and easy to modify for different cases.

2. Leverage Scenario Outlines

Use Scenario Outline to iterate over multiple sets of data:

Scenario Outline: Login with valid credentials
  Given the user "<username>" with password "<password>" exists
  When they log in
  Then they should see the dashboard
  Examples:
    | username | password |
    | alice    | Pass123  |
    | bob      | Test456  |

Ideal for testing multiple combinations with minimal duplication.

3. Use Fixtures or Seed Data for Complex State

For complex applications, define test data using fixtures or seed scripts outside Gherkin (e.g., in JSON, YAML, or DB migrations) and reference it in the scenario:

Given the user “alice” is preloaded in the system

Keeps scenarios clean while centralizing reusable data.

4. Mock External Dependencies

Use mocking or stubbing for external systems (APIs, payment gateways) to provide consistent, reliable test data without relying on live environments.

5. Tagging for Data Contexts

Use tags like @admin, @guest, @premium_user to group tests by data setup or user types. Your test runner or setup hooks can then provision appropriate data.

6. Parameterize Through Environment or Config

Inject test data dynamically via environment variables or configuration files, especially for reusable test suites across environments (dev/staging/CI):

Given a user with email "${TEST_USER_EMAIL}" logs in

7. Clean Up After Tests

Ensure proper teardown or rollback after scenarios to avoid data pollution — especially important in shared test environments.

Managing test data in BDD involves a balance of in-scenario clarity (via tables and outlines) and externalization (via fixtures and mocks) for maintainability and scalability.

17. How to set up tagging for effective BDD test management?

Tags help organize and execute your cucumber tests with precision.

Use Cases organize BDD scripts:

• Group scenarios by feature: @login, @checkout
• Mark tests for CI: @smoke, @regression
• Flag WIP or unstable tests: @flaky, @skip

Example of tags in BDD scenario:

@smoke @login
Scenario: Login with correct credentials

In your cucumber options, you can run a subset:

cucumber --tags @smoke

This enables smarter workflows. For instance, smoke tests run on every commit, full regression tests nightly, etc.

Real-world BDD Scenarios

18. How does BDD help with writing better acceptance criteria?

Instead of vague or overly technical requirements, BDD enforces the use of Gherkin syntax with the Given–When–Then pattern, which captures the purpose of the feature in a structured way. This helps stakeholders, developers, and testers all speak the same language.

Example:

Let’s say your team is developing a login page.

Traditional acceptance criteria might say:

“User must be able to log in if credentials are valid.”

BDD transforms that into a cucumber test scenario:

Feature: Login Page

  Scenario: Successful login with valid credentials
    Given the user is on the login page
    When the user enters a valid username and password
    Then they should be redirected to the dashboard

This BDD scenario is both readable and executable, acting as documentation and test in one. Plus, it links directly to the step definition file in the codebase.

19. How do you prioritize which features to test with BDD?

BDD is best used for functional testing of critical user-facing features–those that embody the behavior your customers care about.

To prioritize:

1. Start with high-risk/high-value features. For example, payment gateways, user registration, or authentication mechanisms.
2. Target areas where misunderstandings often occur. BDD acts as a communication bridge, reducing assumptions by clearly stating the expected behavior.
3. Focus on scenarios with a high number of variations (i.e., where using Scenario Outlines makes sense).
4. Consider features that are part of integration testing, not just unit testing, especially where multiple systems or services interact.

By concentrating BDD efforts here, you ensure your test harness is validating the flows that truly matter.

20. How can BDD be applied to complex systems testing?

In large, interconnected systems, BDD thrives by breaking down complexity into well-defined behaviors. Using background keywords, QA teams can handle shared setup across scenarios and keep tests DRY.

Use Case: Distributed Financial Application

Let’s imagine a microservices-based banking platform that includes account management, transfers, and compliance checks.

Instead of writing convoluted test code, you could write:

Feature: Transfer funds between accounts

  Background:
    Given a user has two active accounts

  Scenario: Transfer within daily limit
    When the user transfers $500 from Account A to Account B
    Then the transfer is successful
    And both balances are updated

Each Gherkin line will map an automation code implementation and step reuse within a step definition, connecting natural language with executable tests.

21. Share an example where BDD significantly improved project outcomes

In a recent e-commerce project redesign, the dev team faced communication breakdowns between product owners, QA, and developers. Requirements often changed mid-sprint, leading to broken tests and late bug discoveries. Once BDD was introduced:

• Cucumber tool was adopted for Gherkin-based specs.
• Features were written in simple English representation.
• Product owners now co-authored cucumber web test case specs with QA.

Positive Result BDD implementation:

Release cycle time was cut by 30%. Test coverage on critical flows like checkout, discounts, and refunds increased dramatically. Teams reported higher confidence in deploying updates, thanks to a living documentation system embedded in the BDD tests.

22. What challenges might teams face when adopting BDD? When you do not recommend it?

While BDD offers massive upside, it’s not for every team or project. Here’s where it can go wrong:

Common Challenges:

• Steep learning curve. Teams lacking technical knowledge may struggle to maintain step definitions or structure scenarios properly.
• Misuse as a testing tool only. BDD is not just a test-writing tool–using it that way defeats its collaborative power.
• Duplicate or bloated step definitions. Without guidelines, teams may end up with hundreds of loosely organized step files.
• Flaky tests due to poor test framework setup, unstable environments, or mismanaged test data.

When Not to Use BDD:

• Very short-term projects where the overhead isn’t justified.
• Solo development efforts with no stakeholder collaboration.
• Internal tools with extremely low complexity.

In these cases, traditional functional testing or exploratory testing may be more efficient.

23. How does BDD facilitate continuous integration and deployment?

BDD plays a critical role in modern DevOps pipelines. When integrated into CI\CD systems like Jenkins, GitLab CI, or CircleCI:

• Cucumber tests become part of the build pipeline.
• Each push or merge triggers the relevant number of scenarios.
• Tagged tests (using @smoke, @regression, etc.) ensure only the right scenarios run per environment.

BDD scripts become a test harness that ensures only working features are deployed to staging or production.

Bonus: Many teams add visual test reports to show business stakeholders which cucumber test scenarios pass/fail–bridging the gap between code and business impact.

Advanced Techniques in BDD

As your team becomes more comfortable with the basics of Behavior-Driven Development, it is natural to move beyond writing simple scenarios and step definitions. At this point, BDD evolves from just a collaboration tool into a powerful software development approach that enhances system reliability, streamlines communication across roles, and improves long-term maintainability.

In this section, we explore advanced techniques that take your BDD practice to the next level. We’ll cover topics like reusing steps across features, applying regular expressions in step definitions, scaling your test framework, and integrating BDD project with your CI\CD pipeline. These strategies will help your team deal with complexity, reduce redundancy, and align testing efforts with real business value.

Why You Should Consider Testomatio for BDD Workflows

One powerful solution designed to enhance BDD practices is test management software testomat.io. This next-generation platform is specifically built to support modern BDD frameworks like Cucumber, CodeceptJS, Playwright and others. It seamlessly integrates with popular automation libraries and CI tools, giving your team full visibility into test results and coverage. With testomat.io, you can:

• organize and manage Cucumber BDD test cases in one centralized place
• create new BDD test cases with advanced BDD editor or AI testing assistant efficiently, or import existing ones outside and automatically convert classic tests in BDD format from .xls files.
• reuse steps easily with Steps Database
• automatically sync with Jira users stories.
• trigger test runs directly from your CI\CD pipelines
• collaborate across QA, developers, and business teams using shared Living Documentation and actionable Reports with public view and free seats

We help teams keep up with delivery demands without sacrificing quality. It shortens the feedback loop, improves communication between stakeholders, and supports test-driven growth.

Conclusion

As interviewers increasingly look for professionals with hands-on BDD experience, knowing how to optimize scenarios, handle flaky tests, and integrate with CI\CD pipelines gives you a competitive edge. More importantly, these skills help you contribute to a healthier, faster, and more reliable software delivery process.

So, if you are automating tests for a login page, refining your test framework, or scaling BDD in your team, mastering the concepts in this article sets you up for both interview success and real-world performance.

The post Top 23 BDD Framework Interview Questions Revealed appeared first on testomat.io.

What is Acceptance Testing

Acceptance Testing is a type of software testing where users, representing the target audience, evaluate whether an application meets their needs and expectations. This is the final stage of testing, QA engineers examine — the system satisfies business requirements and is ready for release.

Acceptance testing is more than just a basic check. It is a complete review process. It takes place in an environment that resembles real life. The method helps to find any issues that might affect its break.

This kind of testing is not the same as other software testing types, as it does not involve only technical aspects. It looks at how well the software manages customers’ preferences and business expectations, including response time.

Acceptance testing asks important questions like:

— Does the software work properly?
— Is it easy to use?
— Do users like it?
— It was designed for what?

By answering these questions, acceptance testing makes sure that the software is more than just technically good, but also relevant for end users.

Terms like functional test, acceptance test and customer test are often used synonymously with user acceptance testing. Although related, it is important to distinguish the differences between these concepts.

* Customer Testing is not a User Acceptance Testing, but about it goes below.

To see the key moments of acceptance testing in action, let’s go together through a practical example

Acceptance Testing Example of Online Banking App

Outcome: The company behind it wants to make sure users can log in safely, move money without errors, and manage their accounts without getting confused.

• Functional testing verifies that the Log in and Transfer Money buttons work, system calculates and sends a request to transfer money correctly, and each of these pieces of functionality separately.
• Customer testing gathers feedback on the app’s usability, reliability, and how well it meets their expectations. How happy are they using it?
• Acceptance testing helps determine if the app genuinely meets users’ goals. Can user log in, view balance, transfer funds, and get a confirmation — all together. How was it convenient, secure and quick?

We need to confirm in our acceptance testing example:

• Login & Security. Makes sure users can sign in and do it safely, protecting their accounts from unauthorized access;
• Accurate transaction processing. Confirms that money is sent, received, and recorded correctly without any mistakes;
• User-friendly account management. Ensures users can easily view balances, transfer funds, and update settings without frustration;
• Meets real user expectations. Checks if the software actually feels useful, reliable, and intuitive for the people using it;
• Fulfills business goals. Verifies that the software supports the company’s main objectives, like improving customer experience or boosting efficiency.

Quick summary of acceptance criteria for our example:

• All critical paths (login, money transfer, basic account management) work without failure.
• No critical or high-severity bugs.
• Users report no major obstacles in completing basic tasks.

As follows, acceptance testing helps catch any final issues before launch, so users get something that truly works for them.

How Acceptance Testing Helps in Software Development

Acceptance testing is an important part of the Software Development Life Cycle (SDLC). It helps understand that only software developed to certain standards is delivered to users. Because it happens after unit, integration, and system testing. Given that, all major bugs should have been found and fixed. Teams conducting acceptance testing in their SDLC lower the chances of releasing software with problems.

A main benefit of acceptance testing is that it can find problems that earlier tests can overlook.

As we’ve seen, other test methodologies typically focus on specific aspects of the software, such as integration or performance. Acceptance testing, on the other hand, evaluates the software from the user’s view. This practice helps define issues in usability, integration, or business requirements that other tests may overlook. It verifies that the software works well, is easy to use, corresponds to business goals and is ready to provide value to users. Thus, with good acceptance tests, development teams can change a software product from just a list of features into something people really want to use and need.

Different Types of Acceptance Checks

Acceptance testing is different and depends on the situation. There are several types. One is operational acceptance testing (OAT), which looks at specific parts of the software. Other common types include user acceptance testing (UAT), business acceptance testing (BAT), alpha testing, and beta testing.

UAT checks if the software is good for the end-user. BAT looks at whether the software fits the business requirements. Alpha testing is done by an internal team that finds bugs before anyone outside tests it. Beta testing includes beta testers who share feedback from a small group of real users. These users try the software in a setting that feels real.

User Acceptance Testing (UAT)

User acceptance testing (UAT) is very important in software development. It makes sure that the final product fits business requirements and user needs. UAT follows set acceptance criteria. During this phase, business users run test scenarios. By doing UAT, organizations can see user satisfaction and test the stability of the product before release. This leads to better quality assurance.

Business, Contract, and Regulation Testing

Acceptance testing is not only about checking that the user is happy. It checks if the software is appropriate for business goals, follows the rules in the contract, and is within the compliance standards. Business acceptance testing (BAT) makes sure that the software fits the business requirements and aims set at the start of development. There is also checking that the software supports business tasks, works well with existing systems, and gives the expected return on investment.

Contract Acceptance Testing (CAT) is a process where software is tested to make sure it meets all the specific requirements agreed upon in a contract between a developer and a client. The goal is to confirm that the software works as promised and fulfills the terms of the contract before it is officially accepted.

Regulatory acceptance testing (RAT) is key for software for healthcare, finance, and government. RAT ensures that the software follows important rules and legal requirements. It also checks the safety of the software. This is very important because of the different countries’ regulations. This process helps the software stay compliant. It makes sure that the software can be used without legal problems or fines.

Balancing User Expectation VS Reality

In software development, people often want different things than what they actually get. However, these expectations don’t always match what the software delivers. Acceptance analysis helps to bridge this gap. Acceptance testing makes sure that the final product follows or even surpasses what users expect; that’s why acceptance testing involves the end-users to help spot usability issues.

It shows where the software can potentially fail the user. This also points out the difference between what users expect and what they really experience. Feedback from users is very important. It leads to better products. It also helps the software fit into real-world situations.

With good acceptance tests, development teams can change a software product from just a list of features into something people really want to use. They focus on the needs of the end users and listen to feedback during the development process.

Improving Software with Acceptance Testing

The information from acceptance analysis is key for the next stages of the development process, when we are improving our product. Teams find out what can and should be improved. They can build on what they have and set goals for the future sprints. Such regular feedback creates a good practice of continual improvement, and software releases become better over time.

Steps in Conducting Acceptance Testing

To do acceptance tests right, you need to be clear and organized. This helps you check everything carefully and get good results.

1. Understand the Software Requirements. Start by making sure you really understand what the software is meant to do. Take some time to go over the functional and business requirements, as this will help you know exactly what to look for when testing begins.
2. Decide What Needs to Be Tested. Next, figure out which parts of the software actually need testing. Focus on the features that are most important to users and that support key business goals. You don’t need to test every tiny detail, only what matters most.
3. Create a Detailed Test Plan. This plan should outline what you’re trying to achieve, how and when you’ll run the tests, who’s involved, and what tools or data you’ll need to get the job done.
4. Choose the Right Testing Method. As you test, decide whether it makes more sense to do things manually or automate parts of the process. Manual testing is great for checking how the software feels and flows. Automated testing works better for repetitive tasks and catching bugs that keep showing up.
5. Define Acceptance Criteria. Entry criteria might include things like having all major features complete or passing earlier tests. Exit criteria could be things like fixing critical bugs, running all the planned test cases, and getting sign-off from key stakeholders.
6. Prepare the Testing Environment. With your plan in place, get the testing environment ready. That means making sure testers have access to the system, the right data, and any instructions they need. Everyone should be set up and ready to go.
7. Run the Acceptance Tests. Now you can begin running your acceptance tests. Follow your test plan, carefully track what happens, and document any issues you run into along the way: bugs, glitches, and anything that seems off.
8. Review Results and Approve or Revise. Finally, once everything’s been tested, sit down with your team and review the results. If the software meets all the criteria and gets the green light from stakeholders, it’s ready to launch. If not, fix what needs fixing and test again until it’s truly ready.

Employing Testing Tools in Acceptance Testing

In today’s fast-paced software development world, choosing the right tools is vital in acceptance analysis. These tools enable teams to write and structure test cases, provide their automation and AI insights, allowing QA teams to test more in less time.

The right tools depend on what the project needs, the technology stack used, the team’s skills and business goals. When teams pick the right testing tools, they can follow a consistent process in the test of acceptance. It also makes it easier for new members to join the team and learn about the checking process. Here are several popular tools and frameworks:

Analyzing Test Results for Improvement

Acceptance testing is important for more than just finding bugs. A key benefit is that it helps make the software better for different use cases. When teams look at the acceptance test results, they gain useful insights about what works and what does not. This allows them to improve software quality and improve the UX.

By watching test results and noting problems, teams can spot patterns. These patterns show where they can improve. The lessons learned can help with future development choices. Teams can work on enhancing current features, increasing performance, and making things easier to use.

Test management software testomat.io provides real-time reporting options for every test you run:

Reports generated with Testomat pull data from different types of testing (like regression, smoke, or exploratory) and organize it into clear visuals like charts, heat maps, and timelines.

They also support useful extras like screenshots, video recordings, and links to bug trackers like Jira. With built-in analytics and support for popular CI\CD tools like GitHub Actions or Jenkins, you can spot issues faster, rerun failed tests with a click, and make smarter release decisions.

Whether you’re a developer, QA engineer, or project manager, Advanced reporting and Analytics can be tailored to your needs, offering either a quick overview or deeper insights into test performance.

Main Roles in Acceptance Testing

Acceptance testing is not only for end-users. It is a group task that includes several people in software development. This group includes developers, testers, business analysts, project managers, and end-users or their representatives. Key roles include:

• Developers. Build the software based on acceptance criteria and perform initial tests to catch bugs early.
• Testers. Design and run tests to check that the software works correctly, meets business needs, and provides a good user experience.
• Business Analysts and Project Managers. Define acceptance criteria and ensure the project aligns with business goals.
• End-Users or Their Representatives. Provide feedback on usability and confirm the software fits real-world needs.

With all these roles, acceptance testing helps deliver reliable software that satisfies everyone involved.

Acceptance Testing Challenges: How to Spot and Fix Them

Successfully managing acceptance testing involves more than just sticking to a plan. Analysis can reveal problems you didn’t foresee. The team needs to be flexible.

They should be ready to change their approach to find good solutions. A common issue occurs when the software behaves differently in the test environment compared to how it should. Let’s explore some common testing obstacles and how to overcome them:

#1: Unclear Acceptance Criteria

If your acceptance tests are vague or poorly written (especially in Gherkin format), it’s hard to tell what success looks like. This leads to confusion and inconsistent results.

What to look for:

• Testers are unsure what to check.
• Different team members interpret test steps in different ways.
• Gherkin scenarios are too broad, inconsistent, or include technical jargon.

How to fix it:

• Use simple, consistent language in your test scenarios.
• Avoid vague terms like “quickly” or “user-friendly.”
• Pair testers with product owners or business analysts to review criteria together.

#2: No Clear Definition of Done

When different team members have different ideas of what “done” means, you end up with features that may work, but aren’t truly complete.

What to look for:

• Teams finish work, but features feel incomplete.
• There’s debate about whether something is ready for release.
• Some items have tests, others don’t — or the level of testing varies widely.

How to fix it:

• Define “done” collaboratively with the team before development starts.
• Include both functional and non-functional criteria (e.g., code reviewed, tested, deployed, documented).
• Write down and agree on the checklist — and stick to it.

#3: Not Enough Stakeholder Input

Testing without stakeholder involvement is like building a house without asking the owner what they want. You might miss essential features or misunderstand priorities.

What to look for:

• Features pass tests but miss business goals or user needs.
• Stakeholders give feedback late — after testing is done.
• No one outside the dev team reviews or approves test coverage.

How to fix it:

• Involve stakeholders early and often, especially during planning and review.
• Invite them to demos, sprint reviews, or even walkthroughs of test results.
• Use their feedback to refine your test coverage.

#4: No Feedback Loops

If testers report issues but no one acts on them — or if developers fix bugs without follow-up — mistakes get repeated.

What to look for:

• Bugs reappear even after they were supposedly fixed.
• Test results are logged, but no one follows up.
• Developers don’t hear from testers (or vice versa) until the end of a sprint.

How to fix it:

• Create a clear workflow for reporting and resolving issues.
• Hold quick daily syncs between testers and developers.
• Use test results to improve both the product and future test scenarios.

#5: Limited Resources

Not enough testers, tools, time, or environments? That means slower testing and missed bugs — especially under deadline pressure.

What to look for:

• Testing is rushed or incomplete near deadlines.
• There aren’t enough people, tools, or environments to run tests properly.
• Only the most critical paths get tested, while edge cases are skipped.

How to fix it:

• Prioritize critical test cases and automate where possible.
• Use shared environments smartly, but manage access to avoid conflicts.
• Ask for help early if testing needs more time, tools, or support.

#6: Hard-to-Maintain Test Suites

Test suites become a burden if they’re brittle or too complex to update regularly.

What to look for:

• Tests constantly break with minor code changes.
• Team avoids writing or updating tests due to time cost.
• Old test cases remain untouched because no one wants to maintain them.

How to fix it:

• Refactor tests regularly to remove duplication and simplify logic.
• Use clear naming conventions and consistent structure across test files.
• Invest in shared utilities and test data builders to make test writing easier.
• Prioritize maintainability over 100% coverage, not every edge case needs automation.

#8: Environment Mismatch

If the test environment doesn’t reflect production, test results lose value.

What to look for:

• Software behaves differently in test vs. production.
• Data in testing doesn’t reflect real-world usage or load.
• Bugs appear only after release, not during QA.

How to fix it:

• Align test and production environments as closely as possible (same OS, services, configs).
• Use production-like test data, anonymized but realistic.
• Automate environment setup to reduce manual configuration differences.

Best Practices for Acceptance Testing

To make sure your software really works for the people who will use it, it helps to follow a few tried-and-true testing habits. Here are some friendly tips to guide you through the process:

• Start early. Don’t wait until the last minute: start defining your acceptance criteria and test cases early in the development process. It saves time and helps avoid surprises later on;
• Involve real users. Bring actual users into the testing phase. Their feedback is incredibly valuable for making sure the software feels right and does what it needs to;
• Focus on what matters most. Prioritize the features that are critical to your product’s success. Testing every little detail is great, but the big stuff should come first;
• Follow a clear process. Use a structured approach with a clear test plan, organized test cases, and a way to track bugs or issues. It helps everyone stay on the same page;
• Use the right tools. A test management tool can make your life easier by keeping everything organized and helping your team stay efficient and focused.

By keeping these practices in mind, you’ll have a much better chance of delivering software that works smoothly, meets expectations, and keeps users happy.

Conclusion

Acceptance testing is very important. It helps make sure that the software is good and matches user needs. Getting everyone involved in the checking process is key. Having clear rules and using smart testing tools, including AI-powered can make the review easier. You can pick between manual and automated tests. What matters most is careful planning and doing everything right.

Similar to a unit test, you may come across different challenges. You will need to check the results and find ways to improve. This helps close the gap between what people expect and what is true. In the end, acceptance checks boost software quality and user satisfaction. It is a vital part of the software development procedure.

Use acceptance testing to provide software that is reliable and corresponds to user requests!

The post The Ultimate Guide to Acceptance Testing appeared first on testomat.io.

This approach helps fully unlock the potential of the testing team and identify issues that might go unnoticed when using well-structured methods. Exploratory testing definition refers to a dynamic and creative process based on the tester’s observations and professional experience.

What is Exploratory Testing?

Exploratory testing is an unscripted approach in software testing which involves simultaneously application learning, test design, and executing in real time. It is spontaneous software analysis. First and foremost, testers rely on their feelings and experience.

Unlike scripted testing, it follows an ad hoc manner. Testers do not rely on predefined scripts. First and foremost, testers rely on their feelings, experience and intuition to detect defects that could otherwise be overlooked using other approaches. Exploratory testing simulates the personal freedom of a professional.

Exploratory testing is a black box testing technique, but it is important not to confuse it with chaotic (or “monkey”) testing, which aims to check the system’s resilience to intentional load and incorrect actions. Exploratory testing, on the contrary, simulates the behavior of a real user, assessing whether the software product functions properly and provides a comfortable user experience.

Functional exploratory software testing approach that combines elements of functional testing and exploratory testing. It focuses on verifying the product’s functionality without pre-created test cases.

Have you asked:

— Is Exploratory Testing always Functional Testing?

Ans: No, like many QA definitions, exploratory testing is used in multiple ways; it might focus on localization and usability issues, too.

Let’s explore the exploratory testing type in the software testing ecosystem in more detail with our comprehensive scheme

Is Exploratory Testing Limited to Functional Testing?

Exploratory testing is a process where the tester freely explores the program, testing it in various ways, entering different data, and performing different actions to understand how it works. During this exploration, the specialist records all detected errors or unusual system reactions.

Exploratory testing techniques complement other testing methods. They are used in parallel with more structured approaches:

• functional testing;
• regression testing.

This provides a complete picture of the software product’s quality.

The tester transforms exploratory test sequences into formal functional testing scenarios, using automated test case documentation tools. This enhances the traditional approach to testing.

Testers can interact with user stories: in exploratory testing, they record defects, add assertions and voice notes, turning a scenario into a test case.

Key Principles of Exploratory Testing

The main characteristics of this approach:

1. minimal dependence on documentation
2. maximum utilization of the tester’s experience and intuition.

Preparing test cases in advance is not mandatory as we talked about before, and having project documentation is also not critical. Instead of relying on pre-existing knowledge about the software product, the tester directly explores the system during the testing process.

Automation in this type of testing is impossible since a QA specialist cannot create scripts for functionality they are not yet familiar with. The exploratory testing meaning lies in its use for a better understanding of the product’s operation. The methods of this approach form the foundation for further planning and the creation of test scenarios and test sets.

Thanks to integration with tools like Jira and test management systems, teams can directly export collected investigations in the form of documented test cases.

Thus, exploratory testing accelerates the documentation process, facilitates modular testing, and ensures instant feedback. James Bach, co-founder of the Context-Driven Testing School, confirms:

Exploratory testing encourages scientific thinking in real time!

The Importance of Exploratory Testing

Exploratory testing allows testers to apply their knowledge and experience in the process of software verification. The primary advantage lies in asking about the main point of: What is exploratory testing in software testing? This technique enables the swift identification of shortcomings and the gathering of valuable insights about the product. Similarly, the ability to rapidly alter testing strategies allows for a comprehensive examination of all functions and the effective resolution of potential issues. But let’s take a look wider:

Exploratory testing is a crucial component of digital product testing.

Benefits of exploratory testing:

1. Early bug detection. Implement this testing approach during the development phase to find errors earlier, ultimately saving costs.
2. Feedback collection. Assess user responses to new features.
3. Uncovering hidden issues. Detect problems related to usability, performance, or functionality.
4. User perspective. Examine the software from the user’s point of view by replicating real-life scenarios.
5. Quick adaptation to changes. Adapt the testing process in real-time, covering more features and adjusting the approach as necessary. Resolve potential issues in a timely manner.
6. Creative approach. Don’t limit yourself to standard bug-finding methods.
7. Use by Agile teams. Apply during rapid development and quick changes in test scenarios.
8. Core functionality. Quickly identify all the features of the product and explain them to the rest of the development team.

Using this approach helps improve software quality, make it more user-friendly, and enhance team productivity.

Exploratory VS Scripted Testing: Key Differences

The scenario testing method is based on pre-developed test cases. In contrast, the second approach relies on the tester’s experience and intuition, allowing for the detection of non-obvious issues. The main differences between these approaches are outlined in the table below.

Both testing methods offer unique benefits and are suited to different contexts. Scenario testing is ideal for automated checks and repetitive tests where precision and compliance with set requirements are crucial. Conversely, exploratory testing provides more flexibility and is better for discovering issues that might be missed with a structured approach.

The best approach is to combine both methods, ensuring a thorough evaluation of software quality and enhancing the product’s reliability and user-friendliness.

When Should Exploratory Testing Be Conducted?

It allows evaluating the quality of software from the perspective of an end user. In the early stages of software development, when QA teams do not have enough time for detailed test case planning, this approach becomes especially useful. It allows one to do it quickly, familiarize oneself with a product or application, identify major issues and provide fast feedback.

When testing mission-critical applications, the exploratory approach helps uncover unique use cases that may lead to severe failures.

Additionally, it can be even used to improve unit testing by:

• documenting executed steps
• using the gathered information for deeper testing in later stages of development

This method is also beneficial for expanding test coverage, as it allows for discovering new scenarios that may have been overlooked in traditional testing.

During iterative application development, exploratory testing is optimal for testing new features, while automation testing focuses on regression testing and checking backward compatibility.

Best Practices of Exploratory Testing

Among them, we highlight:

• Clear definition of goals and objectives for each exploratory testing session.
• Flexible adjustment of the testing strategy based on the specifics of the software and identified risk areas.
• Detailed documentation of testing results in the form of notes, screenshots, and video recordings.
• Active interaction with developers, business analysts, and other stakeholders during testing.
• Balancing exploratory and structured testing for comprehensive coverage.
• Sharing the obtained results and important findings with team members.
• Deep understanding of the target audience and competitive environment to assess user perception of the application’s functionality.

By following these principles of exploratory testing, you will avoid mistakes.

Various Approaches to Exploratory Testing

There are several effective methods for organizing and documenting exploratory test sessions: Let’s now look at the key features of different types of exploratory testing and how to choose the most appropriate option depending on the tasks and context of the project.

1. Session testing organizes the verification for compliance within clearly defined time intervals (from 60 to 120 minutes). During these sessions, the tester focuses on a specific task or objective. All results are recorded in the session report. This method enhances work efficiency and communication. It is useful to add brief summaries at the end of each session for continuous improvement of the testing process. Remember, testing is a process of critical thinking.
2. Flow-based testing organizes tasks by breaking down the system into logical or functional units. The outcomes are recorded as reports or flow diagrams, facilitating progress tracking. These results help prioritize test scenarios. This method is ideal for testing complex systems where various components interact with each other.
3. The scenario-based approach evaluates the product based on scenarios that simulate real user actions or expected system behavior under specific conditions. The tester examines how the product performs, its usability, and reliability according to the provided scenario. Results and feedback are documented in the scenario report or a detailed plan. This method offers a user-centered assessment of the program, checking if it meets expectations and identifying critical issues.
4. Free testing involves no predefined rules or boundaries, allowing complete flexibility. The tester freely explores the system, independently selecting tools. Results are documented in a convenient form (notes, videos, etc.). This approach fosters creativity and the development of the tester’s intuition. It helps uncover hidden errors and edge cases.

When choosing the appropriate method, it is important to consider the specifics of the project, time constraints, and quality requirements to achieve the best result.

Exploratory Testing Process, How to Conduct it

After defining the testing goal and selecting the methodology, it is necessary to find an appropriate tool. The key factors to consider when selecting an exploratory testing tool are:

• Ease of use. This ensures efficiency and eliminates the need for extended training.
• Versatility. Suitable for implementing various methodologies, environments, and scenarios.
• Cross-platform support. It should be functional across multiple browsers and devices.
• Collaboration. Enables seamless interaction between testers and developers.
• Integration with other tools. The tool should easily connect to test management systems, bug tracking, and CI/CD.
• Reports and analytics. Built-in analysis of test results simplifies the evaluation of product quality.
• Support and community. Availability of documentation, forums, and customer support to quickly resolve issues.
• Price. Functionality and cost ratio, availability of a trial period.

Considering these criteria, an exploratory testing tool can be selected that optimally meets the project and team’s needs.

Key stages of the exploratory testing process:

Step #1: Classification of Detected Errors

Highlight typical defects that are commonly found in similar programs.

Categorize by severity and urgency.

Study the causes of these defects and document them.

Create test scripts to check the detected defects.

Step #2: Creating the Test Task (Test Charter)

The test plan should include the following key aspects:

1. Which specific functionalities are to be tested?
2. Which testing methodologies will be used?
3. What types of errors need to be identified (e.g., visual, functional, etc.)?
4. What metrics and indicators should be considered during the testing process?

Step #3:Determining the Testing Duration (Time Box)

Allocate a defined time frame for testing (typically 90 minutes). Testers should remain focused on the task at hand and minimize distractions. If required, the testing duration can be adjusted based on the progress of the process.

Step #4: Assessing the Testing Results

Log any discovered errors in the defect management system for subsequent action. Evaluate the identified issues and assess their effect on the system’s functionality. Prepare a final report that summarizes the key results from the testing.

Step #5: Final Analysis (Debriefing)

Summarize the test results.

Compare the obtained results with the expected ones stated in the test charter.

Decide whether additional testing is necessary based on the findings.

Adhering to these steps will help organize effective exploratory testing, improve software quality, and identify issues that might have gone unnoticed using standard methodologies.

The Role of Exploratory Testing in Agile Development

Benefits of exploratory testing in Agile projects:

1. Real-time decision-making. The flexibility to move away from rigid test cases enables testers to adapt their testing strategy in real-time, quickly detect issues, and provide immediate feedback to developers. This reduces testing delays and mitigates the risk of siloed teamwork, a common challenge in large projects.
2. Supporting automated testing, exploratory testing also allows for evaluating interface usability, the quality of UX content, and the overall user experience.
3. A more thorough product evaluation. Testers often concentrate on executing predefined test cases, limiting the broader system perspective. Even skilled professionals can overlook critical issues. The exploratory approach enables testers to go beyond standard testing, thoroughly investigate the software, and uncover potential problems that might otherwise be missed.
4. Higher tester involvement. Repeatedly executing the same test cases can lead to disengagement and burnout. Exploratory testing offers more room for creativity, fosters experimentation, and makes the process more stimulating, which enhances tester motivation.
5. Enhanced test quality. Experienced testers can broaden the scope of their testing, improving overall coverage. However, thorough documentation of identified issues is key for optimal results. A comprehensive report that outlines the testing approach, actions taken, and outcomes is an essential part of this methodology.

Exploratory testing provides a holistic approach to quality assurance. It not only aids in identifying defects but also allows for flexible adjustments in the software verification process. As a result, product quality improves, delivering a better overall user experience.

Exploratory Testing in a Test Management System

Exploratory testing with the Test Management System (TMS) allows for organizing test tracking efficiently, without losing its flexibility. It might be a session template with predefined fields, focus area, environment, and data without rigid test cases. How testers can document their ideas with testomat.io Markdown editor makes it easy. You may set that bugs are automatically associated with the exploratory session for traceability and the Jira tasks. AI-Detection assists testers in identifying unusual patterns or critical areas for exploratory focus.

Integrating exploratory testing into a test management system provides teams with the flexibility of creative testing while maintaining the organisation of structured QA processes. It bridges the gap between QA and Development teams, making it ideal for Agile teams that prioritise both speed and quality.

The post Exploratory Testing | Full Guide, How to Conduct, Best Practices appeared first on testomat.io.

Nowadays chaos engineering is an effective way to test and enhance applications. By introducing real-life disruptions under control, chaos testing helps businesses discover their weaknesses. This practice allows one to prepare for unexpected failures and handle challenges better.

What is Chaos Testing?

Chaos testing is a controlled method of introducing failures into a system to observe its response under stress. The goal is to see if the system can continue to operate and recover well. The problems we create can mimic real-life situations or exceed them at times.

For example, these issues could stem from performance issues when numerous users try to access the system simultaneously, like stress testing.

On the other hand variations — infrastructure faulty, poor internet, server equipment. Some data problems such as network latency or network outages. Randomly turning off different parts of a system, like smoke testing.

These help clarify the determination of the system’s steady state.

Let’s define Chaos Testing Definition in the QA testing Landscape

In software development, chaos testing is also well-known as chaos engineering testing, even the second definition is more common. Testers who conduct it — chaos engineers. It is crucial in providing a system’s resilience and positive user experience of the end users.

This methodology is different from traditional structured testing types. Primarily, it is stability validation, while traditional testing methods evaluate both functional and non-functional aspects of software. Instead of only checking how a system should work within pre-defined test scenarios, chaos testing shifts testing focus to preventive validation. The most similar testing type is monkey testing.

So, it is a proactive testing method that uses fault injection to conduct safe tests in smaller system parts or users by purposefully detecting weak areas and fixing these areas before they turn into big problems. The most similar testing type is monkey testing.

Meaning of Chaos testing experiment

Regarding experiments, they can include many different things… being easier or harder to implement. Here are a few examples:

Types of Chaos Experiments

• Database or server shutdowns. This means quickly causing failures in the system.
• Custom code injection. We add code to see how it impacts stability.
• Network latency increases. We check how the system works with slow communication.
• Resource usage increases. Pushing CPU or memory to their limits.
• DDoS attacks. This tests the application vulnerabilities when there is a lot of traffic,
  equally to security testing.
• External dependency failures. We see what happens when third-party services don’t work.
• Configuration alterations. We change settings to check how well the system adapts.

Historical context & chaos engineering evolution

Chaos testing began at Netflix in 2010 after they moved to AWS (Amazon Web Services). Before this, they experienced a system outage with their virtual machines. To avoid similar problems, Netflix created a tool called Chaos Monkey. This tool intentionally creates disruptions in the system. In 2012, Netflix made Chaos Monkey available to the public on GitHub under an Apache 2.0 license. Now it is a popular chaos testing framework. It allowed more IT teams to use chaos engineering now. A major development came when Netflix introduced Chaos Kong. This tool showed how valuable chaos testing is during a regional outage of DynamoDB in 2015. Thanks to this testing, Netflix had less downtime than other AWS users more.

Two Major Principles Founded by Netflix:

1. No system should ever have a single point of failure.
2. A single point of failure refers to the possibility that one error or failure could lead to hundreds of hours of unplanned downtime.

The Essence of Chaos Engineering in Software Development

Chaos testing helps teams:

Detect hidden failures before they lead to a negative user experience
Improve system immunity and recovery mechanisms from incidents on the live version
Enhance system resilience for high-availability applications
Better deal with security surprises and possible DDoS attacks
Prevent large breakdowns or service issues
Prepare teams for real-world incidents in advance
Enhance design
Shows teams how to boost their systems overall

What is the Role of Chaos Testing Experiments?

Highlights issues that might happen.
Provides valuable info on the system state otherwise it might be overlooked.
Instantly provides insights that directly influence software enhancement.

End up, it impacts building systems that can meet the growing needs of the digital world and increase customer satisfaction.

Chaos testing and Test Pyramid Layers

Chaos testing identifies vulnerabilities across all layers of the Testing Pyramid and brings system tolerance. This means teams can strengthen system reliability at every level — from individual functions to full application resilience.

Look in detail

#1: Unit Tests + Chaos Testing (Base of the Pyramid)

Objective: Identify and handle failures at the code level before they escalate.

• Unit tests focus on isolated functions or components.
• Introducing chaos at this level means simulating unexpected inputs, edge cases, or error scenarios.
• Tools like Junit (Java), PyTest (Python), or Jest (JavaScript) can be used for injecting faults.

Examples

→ Simulating a divide-by-zero exception in a function.
→ Injection of invalid or corrupted data to test how methods handle failures.

#2: Integration Tests + Chaos Testing (Middle Layer)

Objective: Ensure that services and components interact correctly under failures.

• Integration tests validate data flow and service dependencies.
• Chaos testing at this level includes network failures, API timeouts, or database crashes.
• Use tools like Toxiproxy or Chaos Mesh to inject failures.

Examples

→ Simulating a database connection failure and observing how the application handles retries.
→ Introducing latency in an external API to test fallback mechanisms.

#3: End-to-End (E2E) Tests + Chaos Testing (Top of the Pyramid)

Objective: Assess full system resilience under real-world conditions.

• E2E tests validate user journeys and system-wide functionality.
• Chaos testing at this level involves killing services, reducing resources, and testing disaster recovery.
• Gremlin, Chaos Monkey, and LitmusChaos can be used for system-wide chaos engineering.

Examples

→ Termination of a microservice instance and check if the system auto-recovers.
→ Simulating high CPU/memory usage on a cloud instance to test performance under load.

Chaos Testing Implementation: Step-by-Step Approach

Effective chaos testing follows structure and relies on some important ideas. Planning it properly is important to provide better outcomes. Therefore, chaos engineering involves a systematic process. A key goal is reaching Quality Assurance. Keep on reading to explore its details

#1 Step: Clarify system design

Chaos test cases are based on the system’s design. You need to understand how the system is built and how its parts are connected. This knowledge helps you find failure points. It also lets you create effective test scenarios that focus on these issues.

#2: Identifying Potential System Vulnerabilities

Before you begin experiments, you need to suppose potential vulnerabilities. Look at major parts or connections that could cause huge problems if they do not function properly.

#3 Step: Set High-level Testing Goals

You need to set a few specific goals for what success looks like. Start by deciding what you want to test in your experiment.

Examples of objectives for validation chaos performance testing

— What is the measure system’s availability?
— How fast does it perform?
— How is it secure?

#4 Step: Formulate Hypothesis

Hypothesis is a structured assumption about how a system should behave under specific failure conditions. You must define expectations of what should happen when you inject controlled disruptions.

Typical hypothesis structure

We believe that when [failure condition happens] the system will [expected behavior]

Example Hypotheses within acceptance criteria

If network connectivity between two services is lost, the system will retry requests and eventually recover (Network Partitioning experiment).

#5 Step: Make Evaluation, Risk Analysis and Prioritization

This step empowers you to improve your chaos engineering efforts. Focus on the most important issues first. Think about the damage bugs might cause. Rank them by risk. Keep in mind a blast radius. This means the part of the system that the experiment will impact.

#6 Step: Build your Test Strategy

Structured test plan is vital. A clear document will help you repeat the experiment. It helps in understanding the results. Also, repeat your steps in the future. Now, let’s specify your steps:

1. Write your experiment plan
2. Break into the parts
3. List the possible failures you will examine
4. Write test cases
5. Share what you expect to discover (expected results).

#7 Step: Execute your experiments

The test is carried out in a controlled environment with the system’s monitoring response closely. It is important to document every detail of the experiment. Open detected defects.

Remember, chaos engineering is not only about crashes. It is also about watching how the system behaves in tough situations.

#8 Step: Monitoring Results and Analyzing System Responses

During this phase, detail Reports & Analytics play a key role in building a solid App. They help you see how the system reacts during the test. You can spot any unusual changes during testing.

Good monitoring tools track key metrics while you test. They might include response times and usage, error rates, or other more specific measurements.

#9 Step: Make improvements based on Metrics & Reviews

Review the outcomes, make improvements, and grow if necessary. See if the system handles the issues effectively or if it falls short of what you expected.

#10 Step: Find new factors

See how possible failures might impact the system.

#11 Step: Repeating until the hypothesis is proven

The refined system is tested repeatedly under the defined conditions until it confirms the hypothesis. Chaos testing is about taking your plans and turning them into simple actions again and again.

This case study shows ways to enhance and tells you if your chaos strategy works properly.

Core Principles of Chaos Engineering Behind Effectiveness

Best Practices of Chaos Testing

• Begin with small tests first.
• Then, gradually widen the blast radius.
• Understand the effects better.
• Use the testing Pyramid to maximize the benefits of chaos testing.
• Use real-life data and situations to test how strong the system is.
• Integrate chaos tests into CI\CD to find issues faster.

Mitigating Risks Associated with Chaos Experiments

• Experiments should take place in a controlled setting.
• Inject chaos tests carefully.
• Use safety steps, like automatic rollback systems.
• Predict a plan to stop the experiment if necessary.
• A thorough check before starting can find what might go wrong.

We must manage risks well so that planned issues do not lead to unplanned problems. Start small to test the strength of an App. Do not create problems in the entire system all at once. Begin by checking one program piece that is not very important. This way, you can see how issues occur in one of the parts of the system and how they may impact other parts or settings. You will also improve how you monitor problems and find solutions without putting the whole app in danger.

As your team feel good about it and gains more confidence, you can increase the blast radius. This means you can take on more complex tasks as you gain experience. You can add more components or even use the whole system and target more users.

Pieces of advice are intended for teams new to chaos engineering practices. Moreover, core principles help experienced development teams manage risks more effectively.

Overcoming Common Challenges in Chaos Testing

Chaos testing can perfectly handle challenges in today’s tech world but has also some challenges. One of the main concerns is the danger of allowing failures to occur on purpose in a system.

Common challenges are:

• A big challenge is getting server logs.
• Another issue is having clear ideas to start.
• It is also hard to manage the resources needed.
• To fix these problems, work with DevOps teams.
• Plan experiments carefully and track the results in detail.
• Make sure you create a plan to reverse any changes you make.

Increasing these negative effects allows you to test in a better way and make problems less likely and the system tougher.

Although, the most challenging is getting help from the people involved. Some of them may feel uncomfortable about causing problems on purpose. To solve this, we should explain the benefits of chaos engineering. It is important to show its value by doing controlled tests. This can help build their trust. We do not want any unexpected shutdowns.

Pros and Cons of Chaos testing

Key Tools and Technologies for Chaos Testing

There are many modern tools, frameworks and technologies for chaos testing software. Commonly they use ideas from Netflix innovations and work great in cloud systems. These tools help test different failure situations. Also, mostly they are automation testing tools. The last allows reduce errors made by people and covers more cases when things can go wrong.

Here are some of the best chaos testing tools:

→ Chaos Monkey: This makes it a good choice for teams new to this methodology.
→ Chaos Kong: This tool simulates problems in AWS clouds.
→ Conformity Monkey: This chaos testing tool alerts you about things that are not following the rules.
→ Latency Monkey: This tool adds delays to the network.
→ Doctor Monkey: This one checks and removes instances that are not working well.
→ 10-18 Monkey: This tool tests how the system works with different languages and regions.
→ Janitor Monkey: This tool gets rid of resources that are not being used.
→ Chaos Mesh, Pumba and Litmus Chaos: These tools help test cloud-native and container-based systems.
→ Gremlin. It is a complete platform to see how systems respond to real-world issues. Gremlin has many features. These features include automatic tests and in-depth reports. It also links to popular monitoring tools.

As development teams improve their chaos engineering skills, companies like Microsoft are making it easier to conduct tough tests and manage chaos.

Amazon Web Services (AWS) offers helpful tools like the AWS Fault Injection Simulator and AWS Systems Manager. These tools simplify chaos engineering on the AWS platform.

Putting money into more featured advanced platforms makes sense if the company plans chaos engineering as a lead part of its software development process. Look at the table below, we prepare a short overview:

Ensuring Team Alignment and Stakeholder Buy-In

Successful chaos engineering is not only about technology. It needs teamwork and support from all. It is important to create a culture where development, operations, and security teams work together. They should feel they each have a part in keeping the system strong.

Good communication is key to getting support and trust from the people involved. You should talk often about the goals, methods, and results of chaos engineering experiments. Explain how AI (Artificial Intelligence) can help find and lower risks. For instance, RedHat use-case of selecting test case scope by AI tool and running them with CI\CD.

It is very important to show that identifying and fixing vulnerabilities early can lead to a stable system. This approach can cut down on downtime and make customers feel more satisfied.

When companies show how chaos engineering helps their goals, teams look for insights to make the software better. It creates a culture of continuous improvement

Expanding chaos engineering testing across industries

This practice is essential not only for tech companies but also for banks, government, finance, healthcare, and schools. It is perfect to use chaos engineering for industries that have strict regulations. In these areas, being dependable and following guidelines is vital — what chaos testing successfully ensures.

On the other hand, this type of testing is advisable for large-scale enterprise software and is generally an exception for small or mid-sized web development projects.

Conclusion

In summary, chaos testing is top for today’s software development. It makes systems stronger, even if they run well. They help find problems before they occur. For successful chaos experiments, you need clear goals. Focus on risks and choose the right tools. It is crucial to reduce these risks. Everyone should understand chaos testing for it to be effective.

Remember, being prepared is the best way to handle issues confidently. If you want to start chaos testing, take your first steps today to create a stronger software system.

Would you like help in setting up a chaos testing strategy for your organization? Be free to contact@testomat.io to learn more about our service and test reporting solution.

The post Discover the Power of Chaos Testing Techniques appeared first on testomat.io.

What is test design & its role in the development process?

Test design is part of the quality assurance process, during which test case design in software testing takes place and the sequence of testing actions for a project is determined.

Hmm… Test Design is needed to:

Create tests that detect critical errors
Approach testing with understanding and avoid unnecessary resource expenditure
Minimize the number of tests required to verify the product

The testing team decides how to maximize test coverage with minimal effort.

What Are Test Design Techniques?

Design for test is a key link between the test strategy and the specific tests used to implement the strategy. This process occurs in the context of assigning tests to specific scenarios, and its main aspects can be outlined as follows:

• It is impossible to test everything within the time and budget constraints defined in the technical specifications. A decision must be made on how deeply to dive into testing.
• The more critical the object being tested, the more intensive the checks should be. This is assessed through risk analysis.
• The test strategy helps form a general understanding of what needs to be tested and with what intensity to maximize the consideration of identified risks.
• Depending on the available test base, appropriate test design techniques are chosen to achieve the necessary coverage level.
• The application of these techniques results in the creation of a set of test scenarios that allow for proper execution of the testing task.

In software development, test design techniques specifies the process for creating test cases, which are a series of steps that guarantee the confirmation of a particular function at the end of the development phase. Using efficient test case design techniques gives the project a strong base and improves accuracy and efficiency. Otherwise, there is a risk of overlooking errors and defects during the software testing process.

Testing methods are classified as “black box,” “white box,” and experience-based approaches. More details on this are available in the video.

Types of Test Design Techniques

There is a wide range of approaches to writing test cases. With these methods, you can effectively test all the capabilities and functionality of your software.

Static Test Design Techniques

Static test design techniques include the analysis and review of software artifacts (such as requirements, design documentation, or code) without executing them.

These include:

• Reviews: Formal or informal evaluation of documents or code, such as peer reviews, inspections, or discussions with colleagues.
• Static analysis: The use of automated tools to analyze source code or test of design with the goal of identifying potential issues, such as code standard violations, security vulnerabilities, or maintainability problems.

Benefits of Static Techniques:

→ Allow for identifying defects at early stages, which reduces the cost of fixing them.
→ Contribute to the improvement of documentation, code, and software quality in general.
→ Do not require an executable version of the program.
→ Increase efficiency by detecting errors before dynamic testing begins.
→ Are aimed at identifying defects at early stages of development, which helps reduce the cost of fixing them.

Dynamic test design techniques

These techniques focus on testing the functionality, performance, and behavior of the system being tested.

Dynamic test design techniques include:

• Black Box Techniques: Testing the external behavior of the program without knowing the internal structure of the code. Examples include equivalence partitioning, boundary value analysis, decision table testing, and state transition testing.
• White Box Test Design Techniques: Checking the internal structure, logic, and code of the program. Examples include statement coverage, branch coverage, and path testing.
• Experience-based Techniques: Checks based on the tester’s knowledge, intuition, and experience gained from working on similar projects. Examples include exploratory testing, checklist-based testing, and error guessing.

These test case design techniques are often used together to ensure full testing coverage and improve software quality.

Black Box Test Design Techniques

Black box testing is a method of software testing that eliminates the need to understand the inner workings of the system under test. The evaluation of the system’s overall performance is the main goal. This approach specifically focuses on examining the program’s input data and output results to see if they correspond with the anticipated results.

alt = Black box testing

Black box testing methods are based on using sources such as use cases, user stories, specifications, product and software requirements documentation, which help determine which aspects need to be tested and how to create proper test scenarios. They are applied at all stages of testing, covering both functional and non-functional types of checks.

The main methods of Black box testing include:

• equivalence class partitioning
• boundary value analysis
• decision table testing state transition testing

Equivalence Partitioning

Equivalence class partitioning is a software testing technique that involves dividing objects into groups or classes that are processed and tested in the same way. This approach is used to test ranges of values, input, and output data. Equivalent classes are divided into valid (correct) and invalid (incorrect) ones.

The main principles of creating test design using equivalence partitions are:

→ Each value must belong to only one of the classes.
→ It is necessary to test both valid and invalid classes.
→ Classes can be further subdivided into subclasses if needed.
→ To prevent their values from influencing the test results, invalid classes should be tested independently.

This method requires testing at least one representative value from each class in order to obtain 100% coverage. Because all classes are taken into account, you can obtain full coverage, for instance, by selecting one value from each of the legal and invalid classes. Coverage is not increased by testing more than one value from a single class.

Boundary Value Analysis

The goal of this testing technique is to verify the distinctions between equivalency courses. Only the extreme values — just below, barely above, and right at the boundaries — are tested. This makes ensuring the system responds appropriately to edge circumstances, which are where mistakes most frequently occur.

Problems that occur at the system’s extremes could go undetected if testing is limited to the acceptable range. Users may run into issues at these limits, for instance, if a form accepts ages 18 to 60 but handles edge cases like 17 or 61 erroneously. Boundary value analysis makes sure that these crucial circumstances are thoroughly examined.

Decision Table Testing

This is used when different combinations of test inputs lead to different results. This test design technique is especially useful in the presence of complex business rules, as it helps identify the correct and better test cases. It allows testing whether the system or program can handle all possible input combinations. The decision table consists of conditions and actions, which can also be represented as inputs and outputs of the system. Typically, conditions in the decision table are marked as True/False, specific values, numbers, or ranges of numbers.

The primary goal of decision table testing is to ensure full test coverage without missing any potential interaction between conditions and actions. In this process, it is important to consider whether there is a need to test boundary values.

In such cases, equivalence class analysis and boundary value analysis become important complements to decision table testing.

Once the decision table is created with all combinations of conditions and actions, it can be collapsed by removing the following columns:

• Impossible combinations of actions and conditions.
• Possible but impractical combinations.
• Combinations that do not affect the outcome.
• The minimum coverage for a decision table is at least one test case for each decision rule.

The advantage of this test design technique is simplifying complex business rules by turning them into accessible decision tables that can be used by business users, testers, and developers.

However, there are limitations. Its use can be challenging if the requirements or their descriptions are not clearly developed. Moreover, decision tables become much more complex as the number of input values increases.

State Transition Testing

The state transition technique reflects changes in the states of a software system at different stages of use and over various time intervals. Visual representation of information is easier to perceive compared to textual descriptions, which is why this test case design technique enables faster achievement of full test coverage. It is particularly effective when creating test sets for systems with a large number of state variations and is useful for testing the sequence of events with a limited number of possible input data.

The simplest example of using this technique is testing the login page in a web or mobile app. Imagine testing a system that allows multiple attempts to enter the correct password. If the user enters an incorrect password, the system blocks access.

Such a diagram helps easily correlate possible inputs with expected outcomes. Having a visual representation enhances understanding and ensures the correct connection of states.

Domain Analysis Testing

This test design technique is used when testing a large set of variables simultaneously. It combines equivalence class and boundary value analysis techniques. Domain analysis testing is conducted when multiple variables need to be checked at the same time, unlike testing individual parameters using equivalence classes and boundary values.

— Why is it important to test multiple variables at once?

→ Often there is insufficient time to create separate tests for each variable.
→ Interdependent variables need to be tested when interacting with each other.

Complex systems require special attention and effort from specialists to ensure thorough testing.

Cause-Effect Graph

The cause-effect graph is a method of test design in software testing that highlights the relationship between the result and all factors influencing it. This method is used to create dynamic test cases. For example, when entering a correct email, the system accepts it, while entering an incorrect one results in an error message. In this technique, each conditional input is assigned a cause, and the result of this input is marked as an effect.

The cause-effect graph method is based on gathering requirements and is used to determine the minimal number of test cases that cover the maximum possible test area of the software.

Key advantages include reducing test execution time and lowering testing costs.

Use Case Testing

Helps evaluate the functionality of the system by testing each use case to confirm proper operation.

A use case is a specific interaction between a user (actor) and the software (system), aimed at achieving a particular goal or task. Testers can use this method to check whether all functional requirements are met and whether the software works correctly.

Pairwise Testing

This method allows for a significant reduction in the number of tests by generating sets of test data from all possible input parameters in the system. The essence of pairwise test design is to ensure that each tested parameter’s value is combined at least once with each value of other tested parameters.

Creating the necessary data combinations is a complex task, but many tools of varying quality are available for this purpose.

This method is effective in the later stages of development or in combination with core functional tests. For example, during configuration testing, the main functionality should first be tested across all operating systems with default parameters through Smoke testing or Build Verification Tests. This greatly simplifies the detection of errors, as pairwise testing works with numerous parameters with variable values, making it challenging to locate the issue. If the build testing fails, pairwise testing should be postponed, as many tests will fail, and efforts to optimize tests will be futile.

White Box Test Design Techniques

This approach to software testing emphasizes analyzing the internal logic, structure, and code of the application. It provides testers with full access to the source code and project documentation, enabling a deep examination of internal processes, architecture, and component integration within the software.

Statement Coverage

This technique ensures that every statement in the source code is executed at least once. The method covers all possible paths, lines, and statements in the source code. It is applied for design for test to determine the number of executed statements out of the total statements in the code.

This method promotes early defect detection and ensures the verification of all possible scenarios, allowing for a higher level of test coverage and exhaustive testing.

Decision Testing | Branch Testing

Branch coverage is a code verification metric used in software testing to ensure that all possible branches in the code are executed at least once. It evaluates the effectiveness of test cases in covering various execution paths within the program.

The main focus is on testing all branches or decision points in the code. This ensures that every possible branch (true/false) at each decision point (e.g., conditional statements, loops) is verified.

Path Testing

This approach is applied to design test cases based on the analysis of the software’s control flow graph. It identifies linearly independent execution paths, optimizing the testing process. Path testing uses cyclomatic complexity to determine the number of paths, and corresponding test cases are developed for each path.

This method achieves full branch coverage of the program without requiring coverage of all possible paths in the control flow graph. McCabe’s cyclomatic complexity metric is used to identify all feasible execution paths.

Methods of path testing

• Control flow graph. Converts program code into a graph with nodes and edges.
• Decision-to-decision paths. Identifies paths between decision points in the graph.
• Linearly independent paths. These are paths that cannot be recreated by combining other paths.

Benefits of path testing

• Minimizes redundant tests.
• Concentrates on program logic.
• Helps optimize test scenario development.

Drawbacks of path testing

• Requires advanced programming knowledge to perform.
• The number of test scenarios increases with code complexity.
• Difficulty in creating test paths for complex programs.
• Potential for overlooking certain conditions or scenarios due to analysis errors.

Path testing is an essential tool for ensuring software quality, but its success relies on proper implementation and alignment with the program’s complexity.

alt = Path testing process

Condition Testing

The goal of this set of test design techniques is to create test cases to verify the logical conditions of a program. One of the advantages is ensuring statement coverage across all branches of the program.

Let us consider the key terminology used in conditional testing:

Simple condition is a Boolean variable or an expression that uses a relational operator

→ Relational expression has the form: E1 <relational operator> E2
→ E1 and E2 are arithmetic expressions
→ The relational operator can be one of the following: <, >, =, ≤, ≥

Compound condition includes several simple conditions, Boolean operators (OR, AND, NOT), and parentheses. Conditions that do not contain relational expressions are called Boolean expressions.

Elements of conditions are:

• Boolean operator;
• Boolean variable;
• Pair of parentheses (enclosing a simple or compound condition);
• Relational operator;
• Arithmetic expression.

These elements define the types of possible errors in conditions. If a condition is incorrect, at least one of its elements will be faulty. 

Accordingly, the following errors are possible:

• Incorrect Boolean operator (errors, absence, or redundancy);
• Errors in parenthesis placement;
• Issues with Boolean variables;
• Incorrect relational operator;
• Errors in arithmetic expressions.

The condition testing methodology involves verifying every condition in the program.

Condition testing methods

• Branch testing
• Domain testing
• Boolean expression testing

Thus, this is a white-box testing technique, where test conditions are determined by the results of individual elementary conditions.

Multiple Condition Testing

This method focuses on testing all possible combinations of conditions in a program. It is also referred to as Multiple Condition Decision Coverage (MCDC).

In programs with numerous conditions, it is crucial to test all their possible combinations, as certain combinations may lead to unpredictable behavior or critical errors.

Multiple Condition Coverage (MCC) helps detect these scenarios, lowering the risk of software defects!

As one of the most detailed testing approaches, it instills confidence in the system’s accuracy and reliability. This is especially critical in high-risk fields such as aviation, medical devices, and nuclear energy, where even minor software errors can lead to serious consequences.

To achieve MCC, each condition is tested in both true and false states to verify all possible combinations. Furthermore, each logical decision is examined individually to ensure that all execution paths are covered at least once.

Condition Determination Testing

The ISTQB defines Condition Determination Testing as:

A white-box test technique in which test cases are designed to exercise single condition outcomes that independently affect a decision outcome.

Its goal is to ensure the accurate evaluation of each condition and the precision of the decision outcome based on the combination of these conditions.

Key condition concepts:

• Atomic condition – The smallest unit of a decision that returns either true or false.
• Decision – A point in the code where a choice is made based on one or more conditions.
• Condition coverage – Ensures that each condition is evaluated at least once as true and once as false.
• Decision coverage – Ensures that each decision is tested for both true and false outcomes.
• Condition and decision coverage (CDC) – Combines condition coverage and decision coverage.
• Modified condition and decision coverage (MCDC) – Ensures that each condition can independently affect the outcome of the decision.

By applying the Condition Determination Testing method, development teams can create more stable and reliable software products.

Loop Coverage

This technique ensures the reliability and efficiency of software, especially for parts involving iterative computations. By using this technique, it is possible to:

• Prevent infinite loops. Identify and resolve errors that could cause the program to freeze.
• Optimize performance by identifying bottlenecks in algorithms and increasing execution speed.
• Improve code quality by verifying that loops operate correctly under various conditions.

Key elements of loop testing involve:

• Evaluating loop conditions to precisely calculate the number of iterations.
• Controlling loop variables to ensure proper management.
• Testing boundary values to verify loop behavior at the edges of permissible values.

Thus, using loop coverage testing can help expand test coverage, optimize testing costs, and improve testing quality while identifying and addressing defects, errors, performance issues, and vulnerabilities in the software or system.

Experience-Based Test Design Techniques

This is not a standard approach to software testing, but rather a flexible method that relies on the intuition, skills, and prior experience of the tester. With this approach, the knowledge of developers, testers, and users is transformed into real test scenarios and valuable insights. Collaboration among all participants in the process enables the creation of effective tests that truly matter.

The main advantage of the technique is its ability to identify scenarios that may be overlooked by other, more rigid methodologies. While structured methods are crucial, this approach adds a creative and innovative dimension to the testing process. In today’s landscape, where quality is paramount, it could be the key to the success of your project.

Let’s explore some types of Experience-Based Testing more detail

Error Guessing

This demonstrates the tester’s ability to identify areas within the application that may be prone to failures. By relying on their experience, the tester intuitively pinpoints potential weaknesses and vulnerabilities.

Exploratory Testing

This approach is based on exploration. Testers investigate the application, thoroughly analyzing its functionality using their experience and attention to detail.

Checklist-Based Testing

This approach involves creating a checklist that gathers various functionalities and usage scenarios for verification. The tester gradually checks each item to ensure that all aspects have been covered.

Therefore, experience-based testing is a valuable approach in situations that require flexibility and intuition. By utilizing the testers’ expertise, it reveals scenarios that might be missed by traditional methods. This approach addresses challenges like insufficient documentation or tight deadlines, resulting in a more thorough and efficient testing process.

Comparison of Test Design Techniques

How to choose the appropriate test case design technique? The selection is determined by several factors:

The complexity of the software
Project requirements
Available resources
Likely defect types

It is typically advisable to combine different approaches to ensure comprehensive testing. The chosen method should align with:

The goals of the verification
The key functionalities of the software product
Potential risks

Black-box testing focuses on verifying the software’s functionality from the user’s perspective, based on requirements and specifications. On the other hand, white-box testing focuses on analyzing the internal structure of the program, including the code, architecture, and integration. Experience-based testing is not a traditional method for verifying software – it is an adaptive approach that relies on intuition, skills, and the tester’s previous experience.

Combining all methods ensures comprehensive software quality control, covering both functional and structural aspects. Each approach plays its unique role in identifying and addressing issues at different stages of development. To achieve the best results, it is crucial to adapt testing methods according to your project’s requirements.

Challenges and Best Practices in Test Design

• Complexity. Designing test cases for complex systems with numerous dependencies can be challenging.
• Changing Requirements. Frequent changes in requirements may necessitate constant updates to test cases.
• Time Constraints. Balancing the level of detail with time limitations requires prioritization and efficiency.

To overcome these challenges, it is essential to adhere to the following principles to improve productivity and efficiency.

Best practices in test design

• Clarity and precision. Test cases must be clear and unambiguous.
• Prioritization of critical paths. Focus first on high-risk areas and critical functionalities.
• Reusing test cases. Where possible, reuse test cases for similar functionalities.
• Implementing test automation. Useful for repetitive and large-scale test cases, saving time and improving efficiency.
• Continuous improvement. Regularly review and improve test cases based on test execution results and feedback.

Effective test design ensures that the software testing process  is thorough, efficient, and aligned with the project’s quality goals. By planning and executing test case design techniques, defects can be detected early, product quality can be improved, and client satisfaction can be enhanced.

A Few Practical Examples & Use Cases:

— How It Can Cut Spending and Optimize a Testing Budget

Testing with one user early in the project is better than testing with 50 near the end.

Steve Krug,
a UX professional

Fixing issues early in the development process is much cheaper. For instance, if issues are overlooked during the design phase, their impact can multiply as the project progresses. During development, these errors may become embedded in the program’s core structure, potentially disrupting its functionality. Making major changes to the software architecture after testing, or particularly after the product launch, demands considerable resources and financial investment. This could also result in a loss of user trust due to malfunctioning software.

That’s why it’s important to test each component of the program during its development. In such cases, iterative test case design techniques, typical of agile approaches, demonstrate their effectiveness.

Tools Make Our Test Design Easier

Open-source frameworks are leading among the most popular testing tools. Among them, Selenium, Cypress, JUnit, TestNG, Appium, Cucumber, and Pytest have gained the most popularity and are used for various types of testing.

Test management systems or tools are specialized software that helps quality teams organize, coordinate, and control testing processes. These platforms can integrate with automated testing tools, CI\CD systems, bug-tracking tools, and other solutions.

The market offers a wide variety of test management tools for different budgets and tasks. Here are a few popular options:

• Testomat.io – a tool for full test management with just a few clicks, significantly speeding up the development cycle. It offers “all-in-one” automation.
• Zephyr – used for test management, focused on Agile and DevOps.
• SpiraTest – a universal test management tool that allows planning processes, tracking defects, and managing requirements.
• TestRail – a comprehensive solution with numerous integration capabilities with automation tools and bug-tracking systems. It has powerful reporting features with adaptive dashboards.
• Kualitee – a tool for managing test cases, defects, and reporting. It integrates with various testing systems, including mobile ones.

Depending on the chosen tool, test management systems can significantly ease test design, increase efficiency, promote process organization, improve communication, and provide a complete overview of progress.

Summary

Overall, test creation plays a crucial role in the software development process. This is where theoretical knowledge about testing methods turns into practical, effective checks. By carefully applying proven test design techniques, testers ensure that each release meets the highest quality standards, guaranteeing users a reliable and functional product. Thus, testing is not just about finding bugs, but also confirming the software’s ability to work flawlessly under real conditions, making this stage critical to the success of any project.

The post Test Design Techniques in Software Testing: a Comprehensive Guide appeared first on testomat.io.

Definitely, it is not a good thing when external customers find bugs before your team does. Is it right?

With regression tests at hand, the team can ensure that new code modifications do not disrupt the existing functionalities of your software products. In this article, we will help you understand what regression testing is across the different testing types, why you need a regression test plan and how to write regression test cases.

Regression Testing: What It Is And When We Need It

Regression testing, as a quality assurance practice, a type of software testing is designed for software and testing teams to re-run types of tests to catch bugs in a software app after code changes, updates, or upgrades have been implemented. However, it is more than just rerunning previous test cases. Teams conduct regression testing generally before the application goes to production, aiming to make sure that newly implemented functions are correct, without new bugs or errors and do not cause them in existing system functionality.

As a rule, the testing teams carry out regression testing in the next situations:

• When the team develops a new feature for the software product
• When the team adds a whole new functionality or feature to the software product
• When the team adds patch fixes or implements changes in the configuration
• When the team releases a new version of the software product – mobile or web application
• When the team optimizes the codebase to improve performance

You should note that even minor changes in the code may lead to costly mistakes for the company if they won’t be properly tested. By applying regression testing, testing teams can maintain software quality and avoid the return of previously identified issues. You can find more information about regression testing in our article here.

Regression Test Plan

Before your teams write regression test cases, they should create their regression testing plan in advance. It is a document with a clearly defined strategy, goals, or scope for the regression testing process. Ideally, this plan should include a list of the features or functions the team has to test, the testing methodology (e.g. align to Agile methodology), the testing approach, the necessary resources, and the planned testing result.

Assumptions and Dependencies

The team needs to consider assumptions and dependencies when they design a regression test plan, because they may affect the success of your plan. So, it is important to take into account the following:

Whether the app’s version is stable and no major architectural changes have been implemented.
Whether the test environment is ready to mimic a real-world setup with all required dependencies and resources.
Whether test cases and data are easy to access for each team member.
Whether the test plan documents all the dependencies and assumptions for other teams, because they also need to collaborate and work on the product.

Key Elements of Regression Test Plan

Source

• Test Cases. You need to define every test for regression testing and check whether they carefully validate all system functionalities based on the test scenarios and requirements.
• Test Environment. Here, teams need to specify the hardware and software configuration (app version/OS/database/dependencies) for regression tests.
• Test Data. Teams need to provide accurate and complete test data. This allows them to cover all possible scenarios for the test cases they are going to use.
• Test Execution. Teams need to organize the test runs with the schedule, timeline, and necessary resources such as team composition, hardware, and software tools.
• Risk Analysis. Here, teams need to think of an effective mitigation strategy that will help them to prevent or maybe avoid possible regression testing risks.
• Defect Management. If the team implements defect management into their workflow, it allows them to report, track, and fix bugs that have been found during software testing activities.
• Test Sign-off. Here, teams should set clear criteria and metrics that will help them complete and approve regression tests. Also, it allows them to reveal if the regression testing process is successful or unsuccessful.
• Documentation. In the well-conducted documentation, the team should keep detailed records of test cases, testing data, results of test runs, and defect logs for future review.

Now, you have a comprehensive test plan at hand and can overview the process of how to write regression test cases below.

How To Write Regression Tests: A Step-By-Step Guide

When the teams are going to write test cases for regression testing, they may face some challenges. We hope that this step-by-step guide will make the process easier. Here are some important steps you need to follow when creating a regression test suite:

#1: Identify Test Scenarios For Better Testing Process Organization

In regression testing, you should understand what changes have been made and what new features have been released or implemented. Only by learning the feature requirements and scope can teams consider all potential scenarios. It will help teams define appropriate test scenarios to repeat the validation of existing ones and create new test cases for regression. Based on these scenarios, you can define how the software will perform under specific conditions (such as responding to user actions, protecting sensitive data, and so on) and assess how tested software processes user inputs and handles different data types, etc. With clear and well-defined test scenarios, QA professionals make sure that the regression testing suite effectively achieves its goals.

#2: Specify Test Cases 

At this step, you need to define test scenarios that allow you to move to a detailed test case design. However, you need to remember that the regression test format sometimes differs for tests that have been written with classical or BDD approaches. In most cases, regression tests are not designed from scratch. Teams often use reusable test cases created before or write test cases for new features on their basis. Furthermore, regression tests are often automated but require detailed cases for tests that adhere to specific standards, for instance, BDD regresion test cases in Gherkin’s plain language. These cases for tests will outline the prerequisites, test steps, test data, expected/actual results, status, and notes.

In addition to that, your tests should be easy and simple so that anyone on the testing team can understand what the goal of the test is. With attachments, screenshots, or recordings added, you can make tests easy to understand.

Below you can find a Test Case Example:

If you are testing login functionality, your tests should clearly state the steps, the credentials to use, and the expected outcome, such as successful login.

#3: Prioritize Tests To Understand What To Test First

At this step, after designing tests, it is imperative to focus on test case prioritization based on their risk and impact, critical features for the smoke tests, and the right time to automate and validate them. Just because you need to identify defects that need immediate attention. You can consider modifications that have an impact on core features, or those that significantly change how the application works, which should always be the top priority. You should take into account the following:

• Scope of code change implemented
• Frequency of use
• Historical number of defects
• Interdependency (a situation where one test case depends on the outcome of another one)
• User feedback
• Pain Points

However, the best way to deal with it is to prioritize the tests according to critical and frequently used software functionalities. When you prioritize the tests based on priority, you can make the regression test suite shorter and save time by executing fast and frequent regression tests.

For example, in a banking application, a test that verifies key functionality like account login or transferring funds should be prioritized over a test case that checks the form style.

#4: Use Automation Testing Tools To Speed Up Testing

With test automation tools, you can enhance regression testing. You can avoid the need for manual testing by creating an automated regression test suite. It becomes possible to rerun tests whenever there are changes in the developed software.

Also, you can integrate them with the test case management system like testomat.io with access to a real-time testing dashboard for monitoring the test execution progress and viewing the test results. It will also work as a central place where every team member can be in the know about managing, organizing, and keeping all tests on track.

#5: Analyze Results and Report For Informed Decision-Making

The last step is an in-depth analysis, where you can get important insights for future test runs. With comprehensive analytics generated from testing results, QA managers and other key stakeholders can quantify testing efficiency, assess resource utilization, and measure the effectiveness of the testing process. Testing reports can reveal weak points in the application for in-time adjustments for the software development team.

If your teams start using tips on how to write regression test cases, they can do it effective manner and may:

• Avoid unexpected results from new code changes or modifications.
• Reduce the risk of post-release issues while also making new releases more stable and reliable.
• Produce software with greater quality by detecting and fixing defects very early.
• Keep software stable and reduce the chances of errors.
• Avoid bugs and keep user experience as smooth as possible
• Fix bugs faster and avoid expensive problems related to production.
• Eliminate the need for manual tests, saving valuable time as well as human resources.

Best Practices: How to Write Regression Test Cases Better

A deep understanding of how to write regression test cases is essential for the entire success of your testing process. Here we are going to explore the five transformative steps that help you reap the benefits:

#1: You Need Organize Tests Into Suites

Organizing a solid test suite helps guarantee effective test coverage. If your tests are well-structured, QA teams can find defects targeted to the app’s core functions. Additionally, it helps them speed up test execution and support defect identification. With detailed test suites, testers may focus on relevant and helpful execution of tests rather than wasting a lot of time deciding what to test, where, when, and how. Well-designed test suites allow quality assurance teams to execute tests that generate results. Also, they can identify defects to make sure that the application meets quality standards and customer expectations. The better the organization of the test suites, the faster tests can be executed and results analyzed.

#2: You Need To Apply Version Control

Implementing version control for your test scripts and cases is essential. You may not only monitor changes but also keep consistency. Version control allows you to see who made the modifications, what changes were done, and when. You can also rollback to previous versions if necessary. Furthermore, you can isolate the source of the problem by identifying what update triggered an issue, as well as improve teamwork by making sure that everyone has access to the most recent tests and the history of changes.

#3: You Need To Work together with Software Engineers

The QA engineers perform a series of tests to identify bugs, glitches, and other issues that may affect the performance and functionality of the product. On the other hand, software engineers create the code and implement new features based on project requirements. When working together, they can tackle quality-related challenges and deliver a successful software product. As a result, they can streamline the agile development process, minimize errors, and improve overall product quality.

#4: You Need to Utilize Automation

The QA team runs regression testing as a part of every release – after developers add new features or handle bug fixes. They should re-execute numerous tests after every code change. While code iterations might be frequent and the functionality is large, regression automation may solve this problem. With the development of automated regression testing tool and frameworks, the regression testing process has become more efficient and reliable.
With test case management integration, the QA team, developers, and stakeholders can monitor and analyze test coverage and execution progress as well as discover which areas of the application have been tested, highlight gaps in test coverage, and show the status of test execution (e.g., passed, failed, blocked).

#5: You Need To Make Regular Updates

Here, with ongoing reviews, you can adapt the regression suite to new changes in the software. They will help you identify obsolete test cases, add new tests, and improve existing ones. It can be done by:

• Discussing comments and planning updates on regular meetings
• Carrying out post-release retrospectives in order to evaluate the effectiveness of the test suite
• Track testing results systematically in a test case management system to discover any blockers or opportunities for further improvements.

These tips help you make sure that the regression test suite remains effective, up-to-date, and aligned with the evolving needs of your software.

Ready to write regression test cases with ease?

Even tiny modifications to the code may result in unexpected bugs in the software and lead to problems you were not prepared for. With regression testing and well-designed regression test cases, you can accelerate the testing process, save resources, and keep the product as stable as possible. If you start incorporating tips and best practices from the article, you can not only streamline your test case creation process but adjust them accordingly to fit your specific requirements. Drop us a line if you have any questions about regression tests.

The post How to Write Regression Test Cases? appeared first on testomat.io.

Note! You may also encounter other terms for this type of testing:

• Canary deployment
• Incremental, Phased, Staged rollout
• Canary release

Why canary testing?

The origin of the term of canary testing is tied to the work of coal miners. They used canaries to detect excessive carbon monoxide accumulation in the mines. These birds are more sensitive to toxic gases than humans, and they would die quickly, signaling to the miners that they needed to ascend to the surface.

In the software development context, users who first test new features of a digital solution in production environments act as the canaries in a coal mine.

Canary testing is popular among software developers because, during the process, code changes affect only a small group of users. This minimizes the potential negative impact on the global user experience. It also gives the development team ample time to fix any defects before the software is made available to a wider audience.

How Does Canary Deployment Work?

This process is quite simple and does not require significant resources. As a result, such deployment can be used during every new release that potentially contains critical bugs and poses risks to the broader public. Here’s a look at the standard algorithm for performing this type of testing:

Canary testing occurs in several stages:

#1: Selecting the Canary Group

At this stage, the development team and software testers choose a subgroup of users who will participate in the testing.

It’s important to strike a balance here. The subgroup should be large enough to ensure the reliability of the results, but small enough to minimize risks. Several options are possible:

• 1% to 5% of the entire user base. This option is most commonly used by testers. It allows teams to monitor the behavior of the new release in a real-world environment, without exposing the majority of users to potential issues.
• 0.1% to 1% of real users. This approach may be used for particularly large releases. If no critical defects are found, the test group gradually expands.
• B testing. This is intensive testing of an almost finished version of an app before the final software release. It helps identify as many errors as possible. B testing involves a limited number of users, often selected from within the team.

#2: Setting Up the Testing Environment

This stage involves creating an environment that runs parallel to the real environment but does not include real users. The new version of the software that needs to be tested will be deployed in this environment.

Then, using a load balancer, traffic will be redistributed in such a way that only the selected small group will interact with the new release.

Canary Testing Process and Metrics Evaluation

Once the test environment is set up, users begin interacting with the updated features of the web or mobile apps. At this stage, it is crucial to closely monitor all system metrics:

• error rates;
• response times;
• CPU and memory usage;
• latency, etc.

If any metric rises or falls to an unacceptable level, the canary test is stopped. Users are redirected to the previous version, and the new feature is sent back for further development.

To conduct such tests, use feature flags.

What is feature flag in the canary test?

This is a software development method that allows developers to enable or disable certain features of an application without deploying new code. Feature flags make it possible to grant access to features for different groups of users.

In practice, the canary test flag works like this: If the flag is ON a specific part of the code is executed, and the canary group uses the new feature. If a defect is found, the flag is immediately turned OFF, and the code is bypassed.

This scheme describes how the new functionality handles up to it will not work until the error is fixed, error detected by canary tests.

Evaluation of Testing Results

At this stage, there are several possible outcomes:

1. The results of the canary testing are satisfactory, meaning all metrics are at the desired level. In this case, a release decision is made regarding the feasibility of full-scale deployment.
2. The development team has doubts about the stability of the new release. To gather more comprehensive data, more users are involved in the canary testing. Then, detailed monitoring of the impact of software changes on user experience takes place. Once the desired results are achieved, the final new version is deployed to the production environment, and the testing environment is deactivated.

Here, you can watch a video guide on canary releases from an industry expert: What is Canary deployment?

Why Canary Testing is Effective?

Staged rollout is an effective approach to the development and deployment of software products. This is explained by several advantages of such releases:

• Minimizing Risks. Releasing the new version of the application to a limited number of users allows the development team to fix errors before they affect the global user base.
• Budget Savings. This type of testing guarantees fast feedback. As a result, defects are fixed in the early stages of the SDLC (Software Development Life Cycle), which reduces the cost of fixing them.
• Total Control Over Progress. Incremental rollout involves gradually increasing the number of users participating in the testing. This allows software developers to monitor system performance at each stage of development and track consumer feedback.
• Confidence in the Final Product Quality. Regular canary tests give teams confidence that the final version of the product, deployed to all users, will not contain significant bugs or errors.
• Simplicity of Implementation and Interpretation. Staged rollouts do not require complex infrastructure or maintenance, do not lead to system downtime, and all unsuccessful versions can be easily rolled back to previous ones. Testers also have clear metrics to indicate whether tests have been successful or not.

Canary releases are an excellent method of feature management, requiring minimal investment and significantly reducing project risks. This type of testing allows you to implement code changes without affecting the global target audience.
Mykhailo Poliarush
CEO Testomat.io

How to Determine When Canary Testing Makes Sense?

So, in the previous section, we were able to confirm that staged rollout can bring a lot of benefits to the QA team. However, is it always reasonable to launch such tests? Let’s break down how to determine if implementing canary testing will be justified

• Define the nature of codebase changes. Such tests are most suitable for checking software after high-risk changes, the introduction of experimental features, and fixes that may impact system performance.
• Assess the potential impact on the end user. Run canary tests when the application is being developed for a wide audience and the test group minimizes negative feedback from all users. These tests are also necessary for digital solutions in industries where the cost of failure can be too high, such as in healthcare or finance.
• Analyze the probability of failures. If regressions or bugs occurred in similar rollouts, conduct canary testing to reduce the risk of their recurrence.
• Evaluate the team and infrastructure readiness. A phased rollout can be initiated if everything is ready: automation tools are in place, CI\CD pipelines are set up, and there are resources for monitoring, analyzing, and responding to test results.
• Consider the project’s development approach. Canary testing type of testing aligns with the approach of teams that prefer gradual deployment, meaning the incremental introduction of changes to the codebase.

— Have you confirmed that your project requires an incremental rollout?

Then, you must consider the other side of this testing process — the challenges you may face.

Disadvantages of Incremental Rollout

Along with its many benefits for teams and consumers, this type of testing has some limitations. To optimize QA processes on a digital project, it is important to familiarize yourself with these before the test launch.

→ Impact on Users. Although canary tests only affect a small percentage of real users, they still influence consumer opinions about the product.

→ Susceptibility to Errors Due to Human Factors. Despite having clear criteria for evaluating test results, the analysis process can be quite time-consuming.

QA engineers are actively working on solving this issue by implementing various tools to automate canary analysis. One such example is the Kayenta tool from Google and Netflix.

Automated canary analysis is an essential part of the production deployment process at Netflix, and we are excited to release Kayenta. Our partnership with Google on Kayenta has yielded a flexible architecture that helps perform automated canary analysis on a wide range of deployment scenarios.
Greg Burrell, Senior Reliability Engineer at Netflix

→ Limited Capabilities of This Testing Type. This QA process is not suitable for instance for testing standalone desktop applications, regardless of the device type. Complications may arise if the selected users have different versions of the application or devices.

 Canary Release VS Other Deployment Models

Incremental rollout is not the only model used by teams when releasing new versions of digital solutions. Despite having the same overall goal — minimizing risks — they differ in several ways. Let’s review the main ones.

Blue/Green Deployment

This software release management strategy involves two environments. As the name of the model suggests, they are called blue and green. The first is active and serves users, while the second is designed for the new version of the solution.

How the Model of Canary deployment Works:

• The active blue environment handles all incoming traffic.
• The green environment is where the new version of the software is deployed. This environment does not receive any traffic initially.
• In the green environment, testing of the new version takes place. This may include load testing, performance testing, etc.
• Once the new version’s stability is confirmed, traffic is switched between environments — from blue to green. This can be done at the DNS server level or through a load balancer.
• After the traffic switch, the green environment becomes active, and the blue environment is taken out of service.
• In case any issues are detected, traffic can be quickly redirected back to the blue environment. The rollback process is simple and involves minimal downtime.

Progressive, Rolling Deployment

This is another approach to deployment that helps minimize the risk of errors and downtime. It involves gradually deploying new versions of the product in the production environment by replacing components of the old version with those of the new one.

How the Rolling Model Works

• At the beginning of a rolling deployment, all system components operate under the old version of the application.
• The new version of the digital solution is deployed on a small subset of servers or containers in the production environment.
• Traffic is distributed between all components, meaning that users are served by servers running both the old and new versions.
• The number of components involved in deploying the new version gradually increases. Initially, this may be no more than 10%, but over time it can reach 50% or more. Each stage is accompanied by careful monitoring of system metrics and user feedback.
• If errors occur at any point, a rollback to the previous version can be performed to fix the defect. After that, deployment can resume.
• Once all components have been updated, the deployment is considered complete.

A/B Testing Deployment

This strategy, alongside canary testing, is used to test different versions of the application by real users. A/B Testing goal is not only to evaluate the stability of the new release but also to compare different versions of the application to determine which one performs better. A/B testing is even more marketing tool which indicates product readiness.

How the A/B Testing Model Works

• The team creates two or more versions of the app. For example, these could be different variations of a feature, design, interface, etc. Each of these versions is given a name, such as A and B, or A, B, and C.
• When using the app, users are split between versions according to the approved strategy. For example, 50% of users will use version A, and another 50% will use version B.
• During user interaction, the team tracks certain metrics, such as conversion rate, number of clicks, retention, or audience engagement.
• After gathering sufficient data, the team needs to analyze which version works better.
• If the result is clear, the winning version is deployed for all users. If the winner is not obvious, A/B testing is adjusted and repeated.

The common distinguishing feature of these three deployment models is that they require extensive IT infrastructure to deploy both the new and old versions of the digital product. This is not necessary with a canary test release.

Basic Deployment Strategy

This is the simplest approach to deployment. It is most often used for simple apps, where the main goal is a new release with minimal operational costs. The strategy offers less complex rollback mechanisms compared to more advanced strategies.

The Basic Deployment Strategy works by deploying the new version of the software in the production environment. It replaces the old version all at once, meaning all users get access to the updated version simultaneously.

User Acceptance Testing (UAT)

This is the final stage of the Software Release Life Cycle (SRLC). It involves the verification of the final version of the application by end users and stakeholders. If the digital product works as expected in real-world scenarios, it is considered ready for deployment in the production environment.

Principle of Conducting UAT

• Testing is carried out by end users and stakeholders. During the testing, the product’s compliance with functional and business requirements is checked.
• Real-world scenarios and a realistic testing environment, close to the production environment, are used for testing.
• Testing efforts should not depend on QA or development teams. This helps ensure the most unbiased evaluation.
• The focus of the review is on the entire system workflow, not its individual components. This ensures the seamless operation of the entire application.
• After the testing process is complete, the testing and development teams are promptly notified about any found issues for immediate resolution.

For clarity, we present a comparative table of different deployment models:

The Sense of Agile Increments Development & C\D

Canary deployment is closely related to Agile development methodologies and continuous delivery (CD). Here are the key intersections of these processes:

• Iterative Development. Agile teams emphasize making gradual changes to the product, and canary testing fully supports this idea. It allows deploying small components of the application to ensure that each one works as intended.
• The Importance of Feedback. In Agile methodology, quick feedback from end users and stakeholders is crucial. Canary tests provide real-time feedback.
• Automation of the Deployment Process. CD focuses on automating the deployment process. This ensures quick and reliable delivery of new features. Canary testing easily integrates into the CI\CD pipeline.
• Frequent Deployments. Continuous delivery involves very frequent deployments. A staged rollout ensures that they occur without significant failures and have no impact on users.
• Collaboration Between Teams. Canary testing aligns with this Agile principle, fostering close interaction between teams on the project.

Considering all of the above, it is clear that incremental rollout aligns with the goals and methods of Agile development. In fact, this testing approach actively supports the practical application of its core principles.

Automation of Canary Deployment to Optimize the Process

The deployment model under consideration involves real users in canary testing. However, to optimize the process, it is advisable to automate many of its aspects using specialized tools.

Here are some of them:

• Kubernetes. This platform allows for automating the gradual rollout of updates. Its capabilities include configuring deployment policies, as well as setting up replica sets.
• Spinnaker. A continuous delivery service that easily integrates with various cloud providers and deployment systems.
• AWS CodeDeploy. A tool that can be configured for incremental rollouts and allows automation of the deployment process.
• Service Meshes (Istio, Linkerd, etc.). These tools help distribute traffic between different versions of the software product.

Automating canary releases makes them sequential, fast, and secure. It minimizes downtime, prevents human errors, and guarantees continuous feedback.

Final Thoughts on Canary Testing

Canary testing is used to minimize the risks that arise when deploying a new version of the software to all users simultaneously. It is highly effective and plays an important role in the software development life cycle.

— Do you have any questions? Contact the experts at testomat.io We will be happy to answer each of them

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