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.

An edge case is a problem that can occur when a software program pushes its limits. This can make the program behave in surprising ways or even cause it to crash. Finding and fixing these edge cases is essential: it ensures that the software is strong and dependable, working well in various conditions.

What are Edge Cases in Software Development?

Software development often focuses on the “happy path”, meaning that it looks at situations where everything runs smoothly. In real life, though, users do not always use software as expected, often pushing it to its limits. In edge case situations, different factors mix together and lead to problems beyond the normal use of a product. Here are some common examples of edge cases a tester might come across:

• Login Form: A user enters a 256-character password when the system only supports up to 128 characters. This might cause unauthorized access, the app to crash or reject input incorrectly.
• Shopping Cart: A customer tries to add 0 or 1,000 units of a product to their cart; both values are technically valid, but could expose logic or performance issues.
• File Upload: A user uploads a file with a non-standard extension or an extremely large file size. This tests how the system handles unexpected file inputs or storage limits.

If you ignore edge cases, you are likely driving your product to failure. Not fixing these issues on time can lead to software crashes, data loss, security risks, and, after all, a simply bad UX. When you identify and deal with these problems, you ensure that the customers are satisfied with your product.

Importance of Edge Cases

In software testing, an edge case happens when a situation or input is at the edge or even beyond normal operation: this, in turn, demonstrates flaws in the software’s logic. Make sure to understand the difference between an edge case and a corner case before proceeding with your analysis.

An edge case checks how the software works when one variable is at its highest or lowest value, while a corner case tests several variables at their extreme values all at once.

Edge cases are essential in software development: they reveal weaknesses, find possible points of failure, and ensure the software can deal with unexpected user actions or inputs. When software engineers carefully test edge cases and get rid of the issues, they can improve the quality, reliability, and satisfaction rate of the software a lot.

Comparison of Edge Cases and Regular Bugs

Edge cases are unusual situations that usually affect only a small group of users or devices. Even though they are not very common, edge cases can show important problems in software. Edge cases are different from regular bugs, as they come from special conditions rather than widespread issues.

Edge Cases vs. Corner Cases

Corner cases are more complex than edge cases: they happen when different limiting factors affect each other at the same time.

When you understand these differences, you can focus on and solve issues better. Testing for corner cases means trying to make the code fail: you look at how the code runs and see how different variables work together in tough situations.

Common Types of Edge Cases in Software

Identifying potential edge cases is all about understanding how the software operates. You need to be familiar with the inputs it processes and the environment it runs in. Here are a few examples of edge cases you might come across in the testing process:

• Input Validation. Involves checking for extremely high or low values, special characters, empty fields, or various data types. For instance, a form designed to accept numbers should handle cases where someone inputs zero, negative numbers, or values that exceed the maximum limit.
• Date and Time. This area deals with leap years, time zones, and the adjustments for daylight saving time. It also includes performing date calculations that involve different units of time.
• Resource Constraints. Examining how the software reacts when there’s limited memory, insufficient disk space, or internet connectivity issues.
• System States, Timing, and Performance Edges Analyze how the software behaves under high load, delayed responses, or in rare execution paths — such as race conditions, long-running background processes, or system sleep/wake cycles.
• Permissions and Access Control Verify that users with different roles or permission levels cannot access or execute functions beyond their scope. This includes testing edge roles (e.g., expired sessions, newly granted access) and ensuring proper authorization enforcement.

It’s crucial to test the boundary conditions of an algorithm: examine the limits of what the algorithm can manage to uncover any unexpected behaviors. Remember that edge cases can vary and go beyond these types of edge case examples. They depend on the software’s purpose and its users. That’s why you must pay attention to different types of such issues if you want to develop a reliable application.

Finding and Managing Edge Cases

Some surprising problems can appear while developing, and others might pop up during testing or real use. To find and handle these unique cases, we need different plans. This means we should design carefully, test thoroughly, and stay alert to possible weak spots.
In this section, we will talk about helpful ways to find edge cases early during development. We will also look at best practices for dealing with them to improve software quality and make users happier.

Strategies for Identifying Corner Cases

Effective quality assurance processes are very important, as they help to find edge cases early on. By using clear testing methods (namely, test design techniques), developers can fix potential problems so they don’t affect end users.

It is important to address all edge cases, but when you have limited time and resources, concentrate on the most critical ones first. Test design techniques follow a statistical approach and allow us to theoretically suppose where the problems might be hidden. This way, you will reap the maximum benefit without overspending the resources you have.

Thus, which edge cases to prioritize?

1. Look for potential damage to functionality, user experience, or data integrity.
2. Pay immediate attention to those edge cases that can lead to significant errors or data loss in the software.
3. Consider how frequently a certain edge case happens and deal with the rarer ones last.
4. Employ different types of analysis, such as unit testing, integration testing, system testing, load testing, and obviously negative testing types.

A varied approach will help you focus on edge cases based on when they are discovered. Afterwards, handle the critical issues first to prevent them from turning into large-scale problems later.

Prioritizing Edge Cases For Testing

Not all edge cases need to be tested right away. Our best advice is to start with those cases that have the biggest negative impact on your software, in case they appear. For example, an edge case can cause data loss or break some important features of the system. It should be your top priority.

At the same time, do not prioritise those edge cases that are extremely unlikely or hard to test. Focus on those that are more realistic and likely to occur. Concentrate on the risks first and foremost, and choose which edge cases deserve the most attention.

Role Of Exploratory And Scenario-Based Testing

Exploratory and scenario-based testing can help you find specific problems that do not always show up in standard tests. In these tests, professionals deviate from a strict checklist and explore the product freely, everything they can get their hands on. Here, it is extremely important to follow your instincts and pay attention to the areas you haven’t touched upon before. These tests are especially useful when the product is still in development.

• With exploratory testing, testers approach the system the way a real user might. They try out different features, take unexpected paths, and keep an eye out for anything that feels off or confusing. It’s a hands-on way to quickly spot bugs or design flaws that might otherwise be missed.
• Scenario-based testing takes a slightly different angle. Here, testers walk through specific real-life situations, like making a purchase or resetting a password. These scenarios reflect actual user behavior and help make sure important processes work smoothly from beginning to end.

Both approaches add real value to the testing process. They give teams a clearer picture of how users will experience the product and help catch problems that automated tests might overlook. In the end, they help create a more polished, user-friendly product.

Monkey, Fuzz Testing (Input Validation, Error Handling, Graceful Degradation)

Two other useful techniques for edge checks are monkey and fuzz testing. They also help understand how your software reacts to unexpected situations, more precisely, hidden bugs. Here is a more detailed breakdown:

• In monkey testing, you send completely random inputs to the application, like a monkey pressing any buttons. This method helps reveal the reaction of your app to unexpected or illogical interactions.
• Fuzz testing, unlike monkey checks, is more planned and targeted. In it, you send big volumes of random or invalid data to specific parts of the system to see how well it will deal with them.

Overall, both methods are good for checking input validation and handling errors. By using them, you can answer an important question: Does my system respond to bad input meaningfully or instantly crash? Does it keep working properly even when something goes wrong?

These approaches are also good for graceful degradation: that is, ensuring that even if parts of the system fail, it still keeps performing well as a whole. Both monkey and fuzz testing are great for identifying the user-friendliness and reliability of your system.

Leveraging Past Bug, History Reports Of Past Test Runs, Analytics

One of the most valuable techniques in edge analysis is using past bug reports, test run history, and user analytics. It will help you improve the effort you put into testing and the overall quality of each check. You don’t start every single test from scratch: instead, you dwell and what has already happened and pick up from there.

• Bug reports help you keep track of what was reported wrong before. Usually, they highlight the parts that were prone to errors in the past, and that you have to pay special attention to. Therefore, you spend less time but test more effectively.
• The logs of past tests provide valuable information, like the components that were frequently failing and gaps in test coverage. They are indispensable when prioritizing which areas to test first.
• User analytics shows how people actually use your product, including their favorite features and devices on which they access the software. With this knowledge, testers can simulate scenarios and ensure that all user-favourite features work without gaps.

Combined harmoniously, this information helps testers build a smarter strategy and achieve better testing results faster.

Employing AI And Automated Tools To Detect Anomalies

Using AI tools in edge case testing is getting more and more popular, spreading across various industries and software types. At the same time, not all AI automated tools for testing are worth the hype. It is important to pay attention to their underlying features and see precisely what a certain tool can do for your testing process.

The test management system Testomat.io offers a test workflow with all the core tools gathered under one roof. You can switch easily between automated and manual testing whenever you need, as well as:

1. Centralize your testing assets. Instantly upload all your existing tests into the management system to keep them organized and accessible.
2. Automate with speed. Convert every manual test into an automated one in just seconds, streamlining your QA process.
3. Plan effectively from the start. Especially when edge cases are involved, begin with a well-structured testing strategy to ensure full coverage.
4. Focus on test design quality. Develop a robust, maintainable test design that evolves with your product.
5. Ensure full traceability. Build a traceability matrix to map requirements and track defects throughout the testing lifecycle.
6. Get instant feedback. Run tests instantly and receive real-time results using our integrated Analytics Widget.

Testomat’s test management system makes your testing easier by connecting smoothly with the tools your team already uses, like testing frameworks, CI/CD pipelines, bug tracking tools, and knowledge bases. The system offers a whole AI-powered toolset, which makes your edge case analysis faster, smoother, and more effective.

How To Write an Edge Test Case?

Edge test cases check what happens when users do things at the limits of what a system can handle. These tests help find bugs that show up only in unusual situations. Here’s how to write one in a simple way:

1. Find the Limits: Look at where the system sets rules, like how long a password can be or what numbers are allowed. Then test just below, at, and just above those limits. For example, if a field allows 3 to 20 characters, try 2, 3, 20, and 21 characters.
2. Try Weird or Unexpected Inputs: Use things the system might not expect, like really big numbers, negative values, blank fields, or symbols. This shows if the app handles them properly without crashing.
3. Check “Just in Case” Scenarios: Think about what users might do by accident, like refreshing the page during checkout, uploading a huge image, or typing something strange in a form.
4. Write It Clearly: For each test, write what you’re going to do, what you expect to happen, and what should not happen (like errors or crashes).

Edge test cases help make sure your app can handle unusual situations without breaking. They may not happen often, but they matter when they do.

Edge Test Cases (with unittest)

python

import unittest
class TestIsValidAge(unittest.TestCase):
    def test_minimum_valid_age(self):
        self.assertTrue(is_valid_age(18))  # Edge case: minimum valid
    def test_maximum_valid_age(self):
        self.assertTrue(is_valid_age(99))  # Edge case: maximum valid
    def test_below_minimum_age(self):
        self.assertFalse(is_valid_age(17))  # Just below the boundary
    def test_above_maximum_age(self):
        self.assertFalse(is_valid_age(100))  # Just above the boundary
    def test_negative_age(self):
        self.assertFalse(is_valid_age(-1))  # Extreme invalid case
    def test_zero_age(self):
        self.assertFalse(is_valid_age(0))  # Lower edge case
    def test_large_age(self):
        self.assertFalse(is_valid_age(1000))  # Extreme high edge case
    def test_non_integer_age(self):
        with self.assertRaises(TypeError):  # Let's assume we later enforce type
            is_valid_age("twenty")
# Optional: Run the tests
if __name__ == '__main__':
    unittest.main()

Documentation And Communication Of Known Edge Cases

Keeping track of known edge cases helps teams avoid repeating the same mistakes and keeps everyone better prepared. When you clearly document issues in the software and share them with the rest of the team, it helps you make smarter and more informed decisions.

First, document with TMS what the edge case you’re analyzing is. State what kind of input or action was the cause, and what result was achieved. Did the request break something? If yes, what? if possible, attach screenshots and links to bug reports; this would be extremely helpful. Most importantly, keep your report clear and easy to read, and avoid extra fluff. Anyone on the team must be able to understand what is written in your test case, even if it’s their first time reading about this particular issue.

Create a shared document for the entire team to access. You can also turn it into a project board. Ensure that everyone can access it without issues. This way, future testers will be able to track the progress and see which edge cases have already been revealed and checked and pick up from there.

Practical Use Cases to Uncover Edge Cases

Uncovering special situations needs creativity and careful thinking. Here are a couple of examples for your understanding:

• You need to check a login system for very long email addresses or passwords made only of special characters.
• An online shopping site might end up in unusual situations, like a user trying to buy 10,000 items at once, or a checkout that starts with an empty cart.

These kinds of situations may not happen often, but when they do, they can cause unexpected bugs or even crash the system. Think through all the “what if” scenarios to help your team catch issues before users do. It’s also helpful to look at past bugs or strange user behavior for inspiration. Testing edge cases like these makes the product more reliable and shows that the team has thought beyond just the usual user flows.

Conclusion

To summarize everything said, detecting and taking care of edge cases is one of the most important points in software development. By doing so, you can ensure the best possible level of user experience. You should always have a solid strategy for handling edge cases, as it will help you respond to issues quickly. Constantly learn more about the different types of these issues to be able to react to them on the get-go.

The post A Universal Guide to Edge Cases in Software Development appeared first on testomat.io.

What is Gherkin?

Gherkin is a simple, human-readable plain language, composed in such a way that anyone can understand the written statements, even those with a limited scope of programming knowledge. Gherkin is used in Behaviour-Driven Development (BDD). In other words, Gherkin is the heartbeat of BDD.

It helps development teams write clear scenarios that describe how software should behave from the user’s perspective, actions are equal – Steps.  This allows both technical and non-technical people to work together and stay on the same page, making collaboration easier and ensuring documentation stays accurate.

Cucumber is the most widely used BDD framework. Some popular ones are Behat, Behave, JBehave, CodeceptJS and Codeception.

Why Grerkin Matters in Behavior-Driven Development (BDD)

• Gherkin encourages test-first thinking. Gherkin encourages writing scenarious early, guiding teams to define expected behavior before writing code. It prevents bugs, not just catches them.
• Shared Understanding Across Teams. Rather than relying on lengthy technical manuals or ambiguous user stories, Gherkin provides a formalized way to describe system behavior through conversational language. This simplicity enables teams to align expectations early on, especially who is involved in the development process — not just engineers, but also product owners, business analysts, and QA specialists. It occurs during the Three Amigos sessions, where developers, testers, and business stakeholders collaborate to define what the Definition of Done(DoD) looks like.
• Living Documentation. Gherkin plays a vital role in Behavior-Driven Development by transforming complex requirements into simple, structured documentation.
• Enhancing collaboration. Gherkin, by acting as a living specification, reduces misunderstandings, improves test coverage, and keeps requirements tightly coupled with automated validation. It bridges the gap between business intent and technical implementation, making BDD not just possible but practical.

In short:

Gherkin makes BDD practical — aligning business goals with technical implementation through clear, collaborative, and testable language.

Gherkin in Agile & BDD Workflows

Gherkin focuses on teamwork, taking small steps, and getting regular feedback. This method fits well with Agile practices.

In Agile teams, Gherkin helps connect business and tech teams. It helps everyone understand user stories and acceptance criteria together. This way, Agile teams can deliver value bit by bit and adjust to new needs quickly. Gherkin serves well in Agile and BDD workflows:

• User stories → drive features
• Scenarios in Gherkin → describe behavior of these features
• Automation tools like Cucumber, SpecFlow, or Behave → link Gherkin to real tests

This creates a shared understanding between PO, Dev, and QA. Let’s break it down more:

For example, BDD with Gherkin could also be implemented like this in the Agile Cycle:

As you can see from our visual, the main differences between BDD Agile Workflow and traditional imperative testing are:

→  More traditional Agile testing workflow is more focused on execution rather than behavior.
→  BDD uses Gherkin, a declarative DSL that emphasizes specific behaviors.
→  BDD Agile promotes a shift-left approach. With Gherkin-based acceptance criteria defined upfront, teams embed quality into development before it starts.

Basic Structure of a Gherkin Scenarios

A Gherkin .feature file is structured to describe software behavior using scenarios. It begins with a Feature keyword, followed by a description of the feature. Each scenario within the feature outlines specific examples of how the feature should behave, using keywords like  Given, When,  Then to define the context, actions, and expected outcomes. Here is a breakdown of the structure:

For example, the User Login feature describes how users access the system through the login page. If they enter the correct username and password, they’re taken to the home page. If the login details are incorrect, the system shows an error message to let them know something went wrong.

Feature: User Login
As a user, I want to be able to log in to the system.

  Scenario: Successful Login
    Given the user is on the login page
    When the user enters valid credentials
    Then the user should be redirected to the home page

Features and Scenarios Explained

At the center of Gherkin are Features and Scenarios. A Gherkin feature points out a specific ability of the software. It comes with related test cases and explains how a feature should work in different situations.

• Scenarios serve as test cases.
• Each feature has different scenarios.
• These scenarios imitate how real users behave.
• They explain certain actions and the results you should expect.
• They offer a simple guide on how a system should react to various inputs or situations.

To avoid repeating tests for similar tasks with different data, Gherkin uses Scenario Outlines These are like templates. They allow testers to run the same scenario many times with different data. This way, testers can check everything well while keeping the code simple and effective.

Step Definitions: Given, When, Then

Gherkin syntax uses a simple format called Given-When-Then. This format helps to describe the steps for each test case. It makes it easy to understand the setup, the actions taken, and the expected results in a scenario.

• Given shows where the system starts. It describes what the system looks like before anything happens. This step makes sure the system is prepared for what follows.
• When  tells us about the action that makes the system respond. It includes what the user does or what takes place in the system that changes how it works.
• Then shows what should happen after the action in the When step. It explains how the system should behave after that action, so we can check if it works as intended.

* Take a closer look at this extended code snippet — note how we marked Given, When,  Then as Facts, Past, Present, or Future statements for a better understanding of context.

# Login Functionality

Background:
Given the following user registration schedule:
  | Username | Password | Status   |
  | user1    | Pass123  | Active   |
  | user2    | Test456  | Inactive |
And user1 is a Frequent Flyer member     # <-Fact    

Scenario: Successful login with valid credentials
Given user1 has purchased the following credentials:          # <-Past 
  | Username | Password |
  | user1    | Pass123  |
When the user submits the login form                   # <-Present
Then the user should be redirected to the dashboard     # <-Future

What is an Effective Gherkin Test?

Creating good Gherkin tests isn’t just about understanding the syntax. You also need to follow best practices. These practices make the tests clear, simple to update, and dependable.

It is important to write tests that are short and clear. These tests should show how real users interact with the system. Use simple words and avoid technical terms. Focus on one part of the system for each test. This way, your Gherkin tests will be better and easier to handle.

Advantages of Using Gherkin

Gherkin is a powerful communication tool that brings developers, testers, and business stakeholders onto the same page. By describing behavior in plain language, Gherkin helps teams define, automate, and validate application functionality with less friction and more clarity. Below are the key advantages of using Gherkin in modern Agile and BDD workflows.

Better Communication Across the Team

Since Gherkin uses plain English, everyone, whether they are technical or not, can understand what the software is supposed to do. This helps developers, testers, and business stakeholders stay on the same page and reduces the chances of misunderstandings. It also keeps the focus on the user experience, which leads to more useful related features.

Documentation That Stays Current

Gherkin scenarios are tied directly to automated tests, which means they reflect the software’s real behavior, not just how it was supposed to work. You are not stuck with outdated documents, and your team always has a reliable reference point. These scenarios are version-controlled and stored with the code, so everyone can access and update them easily.

Faster Development and Better Testing

Because Gherkin scenarios can be turned into automated tests, they help speed up testing and give quick feedback during development. Writing tests before building features also helps catch issues early. Since Gherkin fits well with Agile practices, it supports frequent changes and constant improvement.

Long-Term Efficiency and Better Test Coverage

Gherkin scenarios are easy to update as requirements change, which helps lower the time and cost of maintaining tests. They also encourage teams to think through different use cases and edge cases, improving overall test coverage. The structured format allows you to reuse steps across different tests, reducing repetition and making your test suite easier to manage.

BDD Test Case Writing Pitfalls to Avoid: How To Solve Them?

Gherkin makes it easier for you to write tests. However, there are a few common mistakes to remember. These mistakes can make your test cases less effective

By avoiding these mistakes and using these Gherkin strategies, you can build better and more reliable Gherkin tests. This will improve your testing as well as the quality of the software.

How Is Gherkin Linked to Automated Test Code

The main connection of the language with automated test code is through its syntax. It uses a plain text format, operating such keywords as  Given, When, and  Then which are linked to the corresponding automated code that executes all the required steps. Thanks to it, the language stays abstract and readable, which allows non-technical users to understand the scenarios and technical users to maintain the test code.

Popular Testing Frameworks with Gherkin Support

Gherkin is paired with testing frameworks that interpret and run them — the most well-known being Cucumber, which turns real system behavior into automated BDD tests.

Together, Gherkin and these BDD frameworks simplify test automation, improve collaboration, and create living documentation that evolves with your product. Below is a comparison of popular frameworks that support Gherkin syntax:

Framework	Language(s)	Description
	Java, JavaScript, Ruby, etc.	The most widely used BDD tool; executes Gherkin scenarios directly.
	Python	Lightweight BDD framework for Python projects; uses Gherkin syntax.
	.NET (C#)	Native BDD framework for .NET; integrates tightly with Visual Studio. Unfortunately, now it is not supportedrted already.
	Multiple (Java, C#, JS)	Developed by ThoughtWorks; supports markdown-style Gherkin + plugins.
	JavaScript	End-to-end test framework with Gherkin plugin; integrates with WebDriver.
	JavaScript/TypeScript	Combines Jest’s test runner with Cucumber support for BDD testing.

Requirements for the Test Management System:
What Do True Testers Need?

Every test automation with a language has its own set of requirements in order for the analysis to succeed. Everything starts with defining the Agile roles in BDD. Every project must include a team that consists of:

• QAs
• Dev team
• BA (business analysts)
• PM (project managers) or PO (product owners).

Then, there are technical requirements that must be met by the system for integrating Gherkin naturally. The basic criteria include:

• Gherkin-Friendly Editor. The system should let users write, edit, and manage Gherkin feature files with syntax highlighting and support for key elements like Given, When, Then, tags, backgrounds, and scenario outlines.
• Seamless BDD Tool Integration. It should work smoothly with popular BDD tools such as Cucumber or Behave, making it easy to plug into existing testing workflows.
• Automation & CI\CD Support. The platform should connect with CI\CD tools (like Jenkins or GitLab), allow automated test execution, and display test results directly in the system.
• Test Management & Result Tracking. The system should let you track which scenarios are passing, which ones failed, and how they map to defects or bugs, offering a full picture of test coverage.
• Team Collaboration Tools. It should support multiple users working on the same features, with options for comments, approvals, and version history to review what changed and why.
• Reporting & Dashboards. The platform should offer easy-to-read dashboards that show test progress, coverage, and trends, with filters for tags, features, or test status.
• Gherkin also helps with living documentation. This means that the tests will update when the software updates. This is important for development that happens in steps. Because of this, Gherkin is a great tool for teams that want to be flexible and create high-quality software frequently.

Once these requirements are met, the team can proceed with setting up the testing environment and running the very first check using Gherkin.

Test management system testomat.io meets the needs of modern teams in Behavior-Driven Development (BDD) and makes the testing process more practical and powerful by seamlessly integrating Gherkin-style test cases into your workflow. Testomatio’s BDD-friendly UI supports an advanced Gherkin Editor.

Steps Database allows the reuse of steps and shared scenarios, making collaboration easier across distributed teams. Smart, generative AI analyses existing BDD steps across your project and suggests new ones based on them.

Starting with us, you can easily turn your manual test cases outside with a script into BDD scenarious in a minute.

Drop us a line today to learn how we can help you enhance your BDD testing processes that meet the highest standards, contact@testomat.io

How Do Gherkin Scenarios Work with Continuous Integration (CI) & Continuous Delivery (CD)?

Gherkin scenarios integrate smoothly with Continuous Integration (CI) and Continuous Delivery (CD) pipelines, helping Agile teams deliver high-quality software faster. When used with CI\CD, Gherkin scenarios automatically run each time code is pushed, ensuring that new changes do not disrupt existing functionality. This provides early detection of issues, minimizes risks, and ensures that only stable, verified features are deployed. Here is how Gherkin enhances CI\CD practices:

• Automated Test Execution. With Gherkin scenarios written in a BDD framework like Cucumber, tests can be automatically executed as part of the CI pipeline. When developers push changes, the pipeline runs these scenarios, validating that new code aligns with predefined acceptance criteria and doesn’t introduce regressions.
• Immediate Feedback Loop. CI\CD practices emphasize frequent deployment and testing to provide immediate feedback. Gherkin’s clear, business-oriented scenarios allow both technical and non-technical team members to understand results, facilitating prompt discussions and decisions.
• Living Documentation in Real Time. Gherkin scenarios act as living documentation within a CI\CD environment. As the software evolves and scenarios pass or fail, the documentation reflects the latest behavior of the system. This keeps the whole team aligned on current functionality and prevents outdated documentation from leading to misunderstandings.
• Continuous Quality Assurance. By integrating Gherkin-based tests into the CI\CD pipeline, teams can enforce continuous quality checks. Each build goes through comprehensive Gherkin-based testing, ensuring that any issues are detected early and resolved before deployment.

Conclusion

Gherkin is very important as it helps teams work together better and be more efficient. Gherkin has a simple structure and is closely connected with Cucumber, but it is not the same. This connection allows teams to speed up their testing and improve BDD, which means Behavior-Driven development.

Writing clear Gherkin tests and using good practices is key to avoiding common mistakes. This helps make software projects successful. There are many examples in the real world that show how helpful Gherkin can be. It is flexible and is valuable in several industries. You should use Gherkin to make your testing better. Keep learning and creating!

The post What is Gherkin: Key to Behavior-Driven Development 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.

What Is Black Box Testing: Benefits and Limitations

Black box testing is a software testing method where the tester checks how the system behaves without looking at the internal code or logic. It focuses on inputs and expected outputs to make sure the software works correctly from the user’s point of view.

Benefits	Limitations
Easy to use — no coding needed	Can miss hidden bugs in the code
Tests from the user’s view	Limited coverage of internal logic
Great for catching UI issues	Hard to trace the cause of a failure
Works well with large systems	Inefficient for complex logic paths
Useful for non-technical testers	Doesn’t test how the feature is built

Black Box Testing Types

The main purpose of these tests in software engineering is to assess software behavior outside of its internal structure. Experts concentrate on what the system does, how it reacts to different inputs and which outputs it produces, instead of verifying the methods behind it.

Even though every test coverage is limited to the external functionality, nothing helps to define performance and security issues with the same precision. The main types of black box testing for software engineering are the following:

• Functional. It is good for checking that the system behaves as expected in various conditions. The main priorities are the input and output behavior of the program or application.
• Non-Functional. These tests analyse the quality of the system, including its performance and how scalable it is, without concerning itself with the internal code.
• Regression. The regression analysis is crucial to ensure that the most recent updates or bug fixes haven’t broken the existing functionality of the software.
• User Acceptance (UAT). Being the final phase of most tests, it is needed to confirm that the program or application is fully ready for deployment and meets the expectations of the end user.

Being the primary types of black box analysis, these methods do not concern themselves with the application’s internal code. The work is done solely on the basis of the product’s responsiveness, scalability, and performance in stress circumstances.

Black Box Testing Techniques

Various techniques for black box testing are often applied by the development team. Professionals design test cases to assess each piece of software under different conditions and answer the main question: will the program work as expected without compromising on user interfaces?

These techniques include equivalence partitioning, boundary value analysis, decision table testing, and state transition testing. Each type of testing aims to detect all possible performance and security vulnerabilities of mobile and web applications.

Equivalence partitioning

Equivalence partitioning is a smart way to simplify the test by grouping similar types of input together. Instead of checking every possible input, you pick just one example from each group, because if one behaves correctly, the others probably will too.

For instance, if a form only accepts ages 18 to 60, then testing just one number from inside the range and one from outside is enough. It’s a smart way to save time while not missing important issues.

Boundary value analysis

This type of black box testing zeroes in on the “edge cases”: the highest and lowest values a system can handle. These are often where bugs like to hide, so testing right at the boundaries can reveal issues that wouldn’t show up with average input.

Example: This technique focuses on edge cases – the highest and lowest values a system should accept. So if you can transfer between $100 and $5,000, you’d test $99, $100, $101, $4,999, $5,000, and $5,001. These are the spots where bugs are most likely to hide.

Decision table testing

When software has lots of rules or conditions, the decision table approach lays everything out in a clear chart of “if this, then that” scenarios. This exploratory testing type helps testers make sure all possible combinations of inputs and outcomes are covered without leaving anything behind.

To illustrate: Say a website offers free shipping only if you’re a member and you spend over $50, the table lays out all the possible combinations so nothing slips through. It’s especially helpful for spotting the lack of logic.

State transition testing

Some systems behave differently depending on what state they’re in – like a phone being locked or unlocked. The state transition approach is a type of usability testing that checks that when something changes (like entering a password), the system reacts appropriately and progresses correctly and according to the expectations.

Example: Some systems behave differently depending on what “state” they’re in. A login system might change from “logged out” to “entering password” and then to “logged in”, or back out after a few failed attempts. This kind of testing checks that those changes happen the way they’re supposed to.

BlackBox Testing Example: Real World Application

The black box approach is a software testing method that can be applied universally across multiple sectors and industries. The examples below illustrate some of the possible real-world applications of this checking type.

E-Commerce Websites

Imagine you’re verifying the login feature on an online shopping site. You try logging in using different input data: correct and incorrect usernames, blank fields, or even special characters.

The goal is to see if the system meets its functional requirements by allowing valid logins and blocking everything else. While this test looks at the system’s behavior from the outside, it’s different from unit testing, which checks the inner workings of individual components.

Equivalence partitioning reduces hundreds of possible username/password combinations to manageable test groups.

• Valid credentials group: Test one correct login → if it works, all valid logins should work.
• Invalid username group: Test “wronguser123” → catches all invalid username errors.
• Special characters group: Test “user@#$%” → reveals if system properly handles special characters.
• Empty fields group: Test blank username → ensures proper validation messages appear.

Real Issue Found: Instead of testing 1000+ possible usernames, you test 4 groups and discover the system crashes when usernames contain “@” symbols.

Mobile Banking Apps

Picture analysing a banking app to make sure money transfers work as they should before the official launch of the application. You create scenarios using different input conditions: transferring too much money, trying to send funds to a blocked account, or having an insufficient balance.

After each test, you check the test results to see if the app reacts correctly. This kind of analysis usually happens later in the software development life cycle, after all the pieces have come together through integration testing. To achieve the best result, several testing techniques come into play

• Boundary Value Analysis: Test at daily limits ($9,999, $10,000, $10,001). Bug Found: Exactly $9,999.99 transfers bypass fee deduction.
• Decision Table Testing: Check combinations of sufficient funds + valid account + within limits + device trust. Bug Found: Internal transfers (savings to checking) ignore daily limits.
• State Transition Testing: Test interruptions during processing state. Bug Found: App crash during processing deducts money but doesn’t send it – no rollback.

Online Ticket Booking System

Say you’re checking a movie ticket website. You try selecting seats, applying promo codes, and picking awkward time slots to see if the system handles everything smoothly. These kinds of checks help make sure the software application works correctly in real-life situations.

• Equivalence Partitioning: Test available seats, sold seats, valid/invalid promo codes, future shows. Bug Found: Valid promo codes allow booking sold-out shows
• Boundary Value Analysis: Test booking limits (7, 8, 9 tickets if max is 8) and time cutoffs. Bug Found: Can book 9 tickets by adding them one at a time.
• Decision Table Testing: Check seat availability + promo codes + show times + member status. Bug Found: Premium discounts don’t stack with promos but system shows they do until payment.
• State Transition Testing: Test concurrent users selecting the same seat. Bug Found: Two users can both pay for the same seat, overselling shows.

ATM Machine

Think about verifying the functionality of an ATM. You try entering the correct and incorrect PINs, withdrawing different amounts, and checking for proper receipt printing.

You’re watching how the machine responds when someone types into the input field. Since you don’t know exactly how the ATM’s software is written, this is a typical black box approach, but if you had some internal knowledge, it might cross into grey box testing. But, if you choose the black box, a boundary value analysis will be the best technique for you, as it finds critical security and operational limits

• PIN attempts: Test 2nd wrong PIN, 3rd wrong PIN, 4th attempt → discovers card gets retained on 4th attempt instead of 3rd.
• Daily withdrawal limits: Test $499, $500, $501 (if limit is $500) → finds limit enforcement gaps.
• Account balance: Test withdrawal when balance is exactly $20.00 → reveals overdraft issues.
• Real Issue Found: Users can withdraw $501 if they do it as $500 + $1 in separate transactions within the same session.

Online Form Submission

Now, consider analysing an online passport application form. You fill it out with valid info, leave out required fields, and try unusual date formats to see how it reacts. Because personal data is involved, the system also needs strong security testing.

You might even run penetration testing to find weaknesses that hackers could take advantage of. This is a pure black box approach – unlike gray box testing, where you’d have some insight into the system’s internal logic.

• Equivalence Partitioning: Test valid dates, invalid formats, complete/incomplete forms, file uploads. Bug Found: Form accepts February 30th as valid birth date.
• Boundary Value Analysis: Test age limits (17, 18, 19 if 18+ required) and file size limits. Bug Found: Names over 50 characters get truncated silently causing office mismatches.
• Decision Table Testing: Check required fields + valid dates + valid files + citizenship status. Bug Found: Non-citizens can submit applications that get processed unnecessarily.
• State Transition Testing: Test session timeout during document upload.Bug Found: Session expiry loses form data but keeps uploaded files, forcing complete restart.

Black Box Testing Tools

Different system testing tools are employed in the process of black box checks. The main goal of such instruments is to replicate real-world use cases and see if the product meets all user requirements. The primary tool for use case testing is Testomat, a test management system that runs the checks on an automated basis, simplifying the process for specialists tenfold.

Testomat

An innovative test management system,Testomat.io merges together manual and automated testing. It accelerates the development cycle, helping testers complete the analysis from A to Z in just several clicks. Built upon the best practices in software development and QA, Testomat presents an ultimate one-stop-shop solution.

Selenium

Selenium is another popular open-source tool that helps automate tests for websites. You can use it to make sure things like login forms or search bars work correctly across different browsers like Chrome or Firefox.

UFT (Unified Functional Testing)

UFT, formerly known as QTP, is a commercial tool made by Micro Focus. It is a great tool for functional and regression analysis, especially in large enterprises. Let’s say you’re working on a banking app: you’d use UFT to confirm that the entire functionality still works both after major updates and smaller tweaks.

TestCollab

TestCollab is a flexible commercial tool that can be used for both script-based and keyword-driven analysis. It works well for desktop, mobile, and web apps: for example, automating the testing process of an accounting software.

Katalon Studio

Katalon Studio is a user-friendly platform built on top of Selenium and Appium. It supports web, API, desktop, and mobile testing, making it a great choice for checking things like an online store’s checkout process to ensure smooth payment flow. It is one of the best for smooth error guessing and detection at the early stages of development.

Appium

If your focus is mobile apps, Appium is a solid open-source choice for compatibility testing. It works with Android and iOS and supports native, hybrid, and mobile web apps – perfect for analysing something like the full purchase flow in a shopping app.

Ranorex

Ranorex is a commercial tool that’s beginner-friendly and comes with strong reporting features to simplify analysing any software’s external behavior. It supports desktop, web, and mobile checks, and could be used to automate repeated test cases for a desktop healthcare application.

SoapUI

SoapUI is a widely used open-source tool designed for analysing APIs. It allows you to send requests and check responses without needing access to the source code. For instance, verifying that an API returns the right error message when incorrect login details are entered.

LoadRunner

LoadRunner, developed by Micro Focus, is designed for performance and load analysis. It simulates many users accessing the system at once, such as checking a university’s online portal right before registration opens. This helps assess the performance of the system in different states, including in times of peak demand.

Cypress

Cypress is a modern tool made for verifying web applications, especially those built with JavaScript frameworks like React or Vue. It runs directly in the browser, so testers can watch what’s happening in real time – perfect for validating things like page navigation and form submissions. The browser-based analysis is also ideal for verifying the user’s perspective on the product before it goes live.

BrowserStack

BrowserStack is a cloud-based platform that lets you test your site on real devices and browsers without needing to set up any hardware. It’s great for checking how your website looks and behaves across devices. For example, comparing its appearance on an iPhone and an Android tablet.

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Reduce the testing process to just several clicks forever with our swift and compact ready-made solution. Ready to give it a try? Testomat is waiting for your call.

Request a demo meeting to get started with the full potential of the tool.

The post What is Black Box Testing: Types, Tools & Examples appeared first on testomat.io.

According to Capgemini’s report, an automation-first approach in the delivery of quality software products should now be the norm across all quality assurance activities.

However, with new trends coming into the IT landscape, the automation testing landscape has significantly evolved. Many organizations have transformed their approach to test automation processes as well as innovative technologies have brought the latest updates in the software development life cycle.

Now, cloud, artificial intelligence (AI), machine learning (ML), robotic process automation (RPA) and natural language processing (NLP) are undeniably impacting the future of testing.

What drives changes in the future of test automation

There is often a variety of forces that press the company to respond to the changes. Let’s discover what forces shape the future of test automation:

• A need to put test automation in the center of changes.
• A need to change test automation tools to avoid loopholes in the process.
• A need to generate almost 100% test coverage.
• A need to use more innovative test automation approaches to attain desired results.
• A need to carry out the test management process properly.
• A need to outperform competitors by analyzing software products right and left.
• A need to find a more cost-effective solution.
• A need to build a team of interchangeable specialists with a low timeframe and easy learning curve to start automation testing according to Agile methodology.

The future of test automation is about which companies will be able to follow new approaches and apply innovative automation testing tools to meet the changing needs and provide quality software solutions. Furthermore, it is mandatory to remember that test automation requires collaboration and communication among all the people involved in the process. Having that in mind, it is crucial to survey test automation trends, identify which one works for you and make sure that it will lead to success. Additionally, we would like to mention that low-code test automation tools are up-and-coming now, but what are they we are going to overview below.

Top 9 Trends In Software Testing in 2025

Below you can find out top 9 testing trends that help you discover what is the future of automation testing in 2025:

#1:Exploratory testing

With technological advances, we are striving for test automation. However, effective and non-automated testing will be always in need. If teams select exploratory testing and incorporate it effectively, this can significantly change the future of testing and brings the following benefits:

• It allows project owners to gain insights by incorporating test techniques impossible to get with other testing types.
• It allows testing teams to find UI issues faster and achieve more on tight deadlines.
• It requires less time to be prepared and provides a fast bug-detection process at any stage of the software development life cycle.
• It allows testers to uncover edge cases and use them in future testing.

However, you shouldn’t just rely on exploratory testing to test your software product. Only by deploying exploratory testing in tandem with other testing methods can you take the most from your testing efforts and improve results.

#2: Сloud-based Testing

When opting for cloud-based infrastructure, quality assurance engineers have access to the pool of devices that emulate real-world traffic and environments. This helps them push code through a variety of automated tests and eliminate server configurations, deployment issues, etc. In addition to that, they can combine DevOps processes with cloud testing tools and configure them according to the product requirements. Here we have presented some benefits of this type of testing:

• When collaborating in the cloud-based environment, developers and testers can connect in real time, work more efficiently and give feedback faster thanks.
• In comparison to traditional testing, using a cloud-based one allows development and testing teams to more accurately fix bugs and bring software products to market faster.
• Cloud scalability allows dev teams to increase or decrease IT resources as needed to meet changing requirements.
• It provides a cost-effective model: organizations pay for the resources they use only.
• It drives high ROI in terms of great configuration, flexibility and scalability offered.

#3: Continuous Testing

Being a driver of quality, continuous testing allows QA engineers to redefine its strategic role in the organization. If integrated with CI/CD tools, it helps respond to increasing customer expectations with the need for high quality and speed, enables scalability and ensures fast time-to-market. Additionally, it offers the following:

• It allows teams to deliver quality products quicker as well as enhance user experience
• It responds promptly to meet customer satisfaction and usability.
• It helps teams to improve testing efficiency as well as cut down costs.

#4: Microservices Testing

According to recent statistics, 92 % of respondents reported some success with microservices while 54% described their experience as “mostly successful”. Only under 10% described a “complete success.” Definitely, this testing trend will be the next big thing in terms of its flexibility and reusability. Microservices architecture allows development teams to create a product as a suite of small autonomous services formed around a particular business domain. That’s why there is no need for developers to develop and deploy the whole stack at once if any change or update is required. When testing, teams can test each service individually and its functional pieces, monitor the ongoing performance, etc. Additionally, they can incorporate it into a DevOps environment and decrease the risk of fall of the business applications.

#5: In-sprint Automation

With in-sprint automation, software quality becomes the goal of the whole team – a Product Manager, BAs, Project Manager, Development teams, QA teams, etc. It aims to consolidate the entire testing process into one sprint by combining together all the fundamental functions of testing. Additionally, it helps realize quite a few benefits, including the following:

• It provides an opportunity to detect and correct bugs much earlier in the software development life cycle thanks to simultaneous code tests completions and test execution.
• It offers shorter test cycles, better test coverage, and shorter release cycles.
• It improves team collaboration and brings it to a new level,

#6: Crowdtesting

The core concept of crowdtesting is to harness the power of the crowd. This means that companies have their products tested by real users on real devices globally. You can make your software products more testable as well as improve your time-to-market. However, You can use crowdsourcing to scale your automated testing capacity but for the fact that the need arises — ahead of a big release or targeting a geographically distributed audience.

#7: Scriptless Test Automation

Codeless test automation is considered to be the next focus area for Agile and DevOps. That’s why more companies are going scriptless. This revolutionary approach provides them with a powerful interface for developing automation suites. Testers and developers can automate test cases without thinking about coding. What’s more, they can obtain results faster and avoid the manual testing effort to create initial test scripts. Testers only need to indicate the testing steps instead of having to write actual code. Additionally, they can focus on finding new and innovative ways to automate. Let’s consider the benefits it provides:

• It helps engage all the team members as well as other subject matter experts in the testing process.
• It helps uncover maximum bugs in the early stage of the software development life cycle.
• It requires hassle-free maintenance even in large automation test suites.
• It significantly reduces the cost of automation.

#8: NLP-based automation

Being one of the scriptless test automation trends, NLP allows testers, developers or project managers involved in the testing processes can develop, edit, manage, and automate test cases and tightly integrate them into the dynamic software delivery pipeline. As you see, there is no need to have any special skills for the team members to perform such a testing process. With an NLP-based approach, you can do the following:

• Provide a more efficient automated testing process and increase test coverage by almost 100%.
• Spend less time on writing test cases.
• Increase the productivity and re-usability of your tests.

#9: AI-driven testing

The use of artificial intelligence in testing simplifies the whole software development lifecycle. AI-powered tools help minimize the tedious and manual aspects of software development and testing while automating the entire process. Being a fairly new concept in the testing process, AI-based tools in automation testing prove to be more pertinent than ever. Here is how an AI-driven test automation solution adds value to your testing process:

• It speeds up the test cases creation with the ability to imitate test case writing style.
• It allows specialists to create test cases more effectively and easily by identifying existing and reusable components as well as removing or skipping unnecessary ones.
• It helps analyze the company’s product right and left.
• It provides greater test coverage.
• It helps prevent errors across a variety of testing activities.
• It helps delegate time-consuming and challenging maintenance processes to AI.

If test automation is today’s hot commodity, where do you see the future of testing and how can you harvest it? The right answer is to identify your business requirements, opt for the right test automation approach, select suitable automation testing tools and create conditions in which automation can produce desired results.

Are you aware of the fast movement of technologies in autotesting?

With rapid changes, fierce competition and digital transformation, automation is no longer a “nice to have,” but a “must have” competency to deliver high-quality products fast. That’s why it’s mandatory for companies to opt for test automation and stay ahead of key software testing trends.

However, testing automation is not always equal to the use of the latest technology only — it’s about agile teams, collaboration and communication, well-defined testing strategy, a properly organized test management, a smooth CI/CD workflow and DevOps pipeline. Highly innovative companies, no matter how large or small, should take it into account to drive innovation in automation testing. This will greatly increase your chances of providing the exceptional software products your customers expect.

The post Future of test automation in 2025 appeared first on testomat.io.

There are several factors that can influence the effectiveness of test management, including:

• The quality and experience of the testing team: The skills and expertise of the testing team can significantly impact the effectiveness of test management. A highly skilled and experienced testing team is more likely to be able to identify and fix defects in the software, as they have a deeper understanding of the software and testing techniques. They are also better equipped to handle any challenges that may arise during the testing process.
• The quality of the test plan: A well-defined and comprehensive test plan is essential for successful test management. A quality test plan should outline the scope and objectives of testing, the stakeholders and testing team, the test approach, the test schedule and resources, and the risk assessment. It should also be flexible and adaptable, allowing for changes and updates to be made as needed.
• The resources and support provided for testing: Adequate resources and support are necessary for effective test management. It is also important to ensure that the testing team has access to the necessary training and support to perform their duties effectively.
• The testing tools and techniques used: The choice of testing tools and techniques can significantly impact the effectiveness of test management system.

The Test Management Process

Test management involves a number of important steps to make sure the testing process goes smoothly. These steps include making a plan for testing, designing the tests, carrying out the tests, reporting on the results, and documenting what happened. Finally, test management also includes wrapping up the testing process.

Test Planning

The first step in the test management process is to define the scope and objectives of testing, identify the stakeholders and testing team, and develop a test plan. It is also important to estimate the resources and timeline needed for testing and identify any potential risks or challenges.

A quality test plan is a crucial part of the test management process, as it outlines the testing activities and resources needed for a project. There are several key factors that contribute to the quality of a test plan:

1. Scope and objectives: The test plan should clearly define the scope and objectives of testing, including what is to be tested and why it is being tested. This helps to ensure that the testing effort is focused and aligned with the goals of the project.
2. Stakeholders and testing team: The test plan should identify the stakeholders and testing team, including their roles and responsibilities. This helps to ensure that everyone involved in the testing process understands their role and how they contribute to the overall success of the project.
3. Test approach: The test plan should outline the approach to testing, including the testing techniques and tools that will be used. This helps to ensure that the testing effort is efficient and effective.
4. Test schedule and resources: The test plan should include a schedule for testing and a list of the resources needed, including hardware, software, and personnel. This helps to ensure that the testing process is well-organized and has the necessary resources to be successful.
5. Risk assessment: The test plan should include a risk assessment, identifying any potential risks or challenges that may impact the testing process. This helps to ensure that any potential issues are identified and addressed in advance.

Test Design

The next step is to create test cases and test data that will be used to evaluate the functionality and performance of the software. It is important to select the appropriate testing techniques and define the acceptance criteria for testing. The test environment and infrastructure should also be considered at this stage.

Whitebox test design techniques

Whitebox test design techniques are methods used to test the internal structure and behavior of a software system:

1. Statement coverage: This technique involves testing every statement in the software to ensure that it has been executed at least once.
2. Branch coverage: This technique involves testing every decision branch in the software to ensure that all possible outcomes have been tested.
3. Path coverage: This technique involves testing all possible paths through the software to ensure that every part of the software has been tested.
4. Condition coverage: This technique involves testing every Boolean expression in the software to ensure that all possible outcomes have been tested.
5. Loop coverage: This technique involves testing all loops in the software to ensure that they are working correctly.
6. Data flow testing: This technique involves testing the flow of data through the software to identify any defects or issues.
7. Mutation testing: This technique involves introducing small changes (mutations) to the software and re-running the tests to ensure that the changes have been detected.

Blackbox test design techniques

Blackbox test design techniques are methods used to test the functionality and behavior of a software system without knowledge of its internal structure:

1. Equivalence partitioning: This technique involves dividing the input domain into partitions and testing a representative value from each partition to reduce the number of test cases needed.
2. Boundary value analysis: This technique involves testing the values at the boundaries of the input domain to identify any defects or issues in the software.
3. Decision table testing: This technique involves creating a table that maps the input conditions and output actions of the software, and testing each combination to ensure that the software is working correctly.
4. Use case testing: This technique involves testing the functionality of the software from the perspective of the end user, using real-world scenarios to test the software.
5. Exploratory testing: This technique involves testing the software in an unstructured manner, using the tester’s knowledge and experience to identify defects and issues.
6. User acceptance testing: This technique involves testing the software with real users to ensure that it meets their needs and expectations.

Test Execution

During the execution phase of test management, the test cases that have been designed are run according to the test plan. This involves setting up the necessary hardware and software, preparing the test data, and executing the tests. The tests may be run manually or automated using testing tools and frameworks.

Once the tests have been run, the results are analyzed to determine if any defects or issues were identified. Any defects or issues that are found should be documented and tracked using a defect tracking system. This helps to ensure that the defects are addressed in a timely manner and that the software meets the required quality standards.

It is also important to review the results of the tests to identify any trends or patterns that may indicate broader issues with the software. This can help to identify areas where the software may need further testing or improvements.

Test Reporting and documentation

Test results should be thoroughly documented in the form of reports, which should include information such as the number of test cases run, the number of defects found, and the status of the defects (e.g. open, closed, deferred). The reports should also include any relevant details or observations from the testing process, such as any issues or challenges that were encountered.

The reports should be shared with stakeholders, such as the project team, management, and customers, to keep them informed about the testing process and the quality of the software. This can help to ensure that any issues or defects are addressed in a timely manner and that the software meets the required quality standards.

In addition to documenting the test results, it is also important to document any issues or defects that were found during testing. This may involve using a defect tracking system to record the details of the defects and track their progress through the resolution process. By thoroughly documenting the test results and any issues or defects found, organizations can improve the transparency and accountability of the testing process and ensure that the software meets the required quality standards.

Closure

After testing is done and any necessary documentation is completed, the test process is wrapped up. This may involve closing out any open defects or issues and completing any final reporting or documentation.

It is also important to review the test process and identify any areas for improvement. This may involve conducting a post-mortem analysis or retrospectives, which can help to identify any problems or challenges that were encountered during the testing process and suggest ways to improve the process in the future.

Benefits of Test Management

Effective test management can bring several benefits to the software development process, including:

Improved quality: By identifying and fixing defects early in the development process, test management helps to ensure that the software meets the required quality standards. This can enhance user satisfaction and loyalty.

Enhanced reliability: Test management helps to reduce the risk of failures or crashes in the software, improving its stability and performance. This can enhance the reputation of the software and the company.

Increased efficiency: Test management can save time and resources by identifying and fixing defects early in the development process. It can also enhance communication and collaboration among the testing team and stakeholders, streamlining the testing process through automation and other techniques.

Challenges and Best Practices

By identifying and fixing defects early on, testing can save time and resources and improve user satisfaction. However, to achieve these benefits, it is important to follow best practices for test management.

One key best practice is to define clear objectives and scope for the testing effort. This involves identifying the stakeholders and testing team, defining the test approach, and establishing the test schedule and resources. By clearly defining the objectives and scope of testing, organizations can ensure that the testing process is aligned with the goals and needs of the project and that the right resources are dedicated to testing.

Another important best practice is to use a risk-based approach to testing. This means focusing testing efforts on the areas of the software that are most important or have the highest risk of defects. By prioritizing testing efforts in this way, organizations can ensure that the most critical areas of the software are thoroughly tested and that defects are identified and addressed as soon as possible.

Planning for testing early in the development process is another important best practice. By starting the testing process early on, organizations can identify and fix defects earlier in the development process, which can save time and resources and improve the quality of the software.

The choice of testing tools and techniques can also significantly impact the effectiveness of the testing process. It is important to carefully evaluate the various options available and select the ones that are most suitable for the specific needs and goals of the project.

Test automation can also be a useful tool for improving the efficiency and effectiveness of the testing process. By automating the execution of test cases, organizations can reduce the time and effort required to run tests. However, it is important to carefully consider the benefits and limitations of test automation and use it appropriately.

Finally, adequate resources and support are essential for the success of the testing process. This may include things like hardware and software, and personnel, etc.

Agile test management

Agile test management is a way of managing the testing process in an agile software development environment. Agile development is a method of building software that focuses on quick delivery, continuous improvement, and teamwork among different functional groups. Testing is an important part of the agile development process and is done throughout the development cycle, not just at the end. Testing and development teams work together to make sure the software meets the required quality standards, and automated testing tools are often used to help speed up the testing process.

Agile test management involves a number of practices and techniques that are designed to support the agile development process, including:

• Continuous testing: Testing is carried out throughout the development process, rather than being left until the end. This allows developers to identify and fix issues early on, resulting in a higher quality product.
• Test-driven development: Developers write tests for new code before writing the code itself. This helps ensure that the code meets the requirements and works as intended.
• Automated testing: Automated testing tools are used to run tests quickly and accurately, allowing teams to test more frequently and get faster feedback on the quality of the software.
• Collaboration between testing and development teams: Testing and development teams work closely together to ensure that the software meets the required quality standards. This may involve regular meetings, collaborative planning, and shared goals.
• Continuous integration: In continuous integration, code changes are automatically built, tested, and deployed to a staging or production environment. This allows teams to detect and fix issues quickly, and ensure that the software is always in a deployable state.
• Exploratory testing: Exploratory testing is a flexible, dynamic approach to testing in which testers explore the software, looking for defects and trying to break it. This allows teams to uncover issues that may have been missed in more structured testing approaches.
• User story testing: In agile development, user stories are short, high-level descriptions of a feature or requirement. User story testing involves testing each user story to ensure that it meets the requirements and works as intended.
• Acceptance test-driven development: In acceptance test-driven development (ATDD), teams define acceptance criteria for a user story before writing the code. This helps ensure that the software meets the needs of the end user.

Test Management Software

Test management software is a tool that helps organizations plan, design, execute, and track the testing process for their software development projects. It provides a centralized platform for managing all aspects of testing, including test planning and scheduling, test case design and execution, defect tracking, and reporting and documentation.

So, what does test management software do exactly? Essentially, it helps teams coordinate and execute the testing process for a software project. Test management software can be used to create and manage a test plan, including setting up test schedules and assigning tasks to team members. It can also provide tools for designing and executing test cases, including support for creating and managing test data and automating test execution. In addition, test management software can provide a centralized platform for tracking and managing defects, including support for assigning defects to team members, prioritizing defects, and updating the status of defects as they are resolved.

How to Choose Test Management Tool

Choosing the right test management software is an important decision for any organization. The right tool can help streamline the testing process and improve the quality and reliability of your software, while the wrong tool can create unnecessary challenges and inefficiencies.

So, how do you choose the right test management software? Here are some factors to consider:

1. Identify your needs: Before you start evaluating different options, it’s important to have a clear understanding of your organization’s specific needs and goals. This might include things like the size and complexity of your software projects, the types of testing you need to support, and the level of integration with other tools and processes you require.
2. Research different options: There are many different test management tools on the market, each with its own set of features and capabilities. Take the time to research and evaluate different options to determine which ones might meet your needs.
3. Consider your budget: Test management software can vary widely in terms of cost, so it’s important to consider your budget when making a selection. Keep in mind that the most expensive option may not necessarily be the best fit for your needs.
4. Evaluate the features and capabilities: Once you’ve narrowed down your options, take the time to carefully evaluate the features and capabilities of each tool. This might include things like test case design and execution, defect tracking, integration with other tools, and reporting and documentation capabilities.
5. Test it out: Once you’ve identified a few potential options, consider setting up a trial or demo of the software to get a feel for how it works in practice. This can help you make a more informed decision about which tool is the best fit for your organization.

There are a variety of test management tools on the market. Test management tools list is not comprehensive, we just want to show you most popular one:

1. Zephyr: Zephyr is a comprehensive test management solution that helps organizations streamline their testing efforts and improve the quality of their software. It includes features such as test case design and execution, defect tracking, and integration with agile development frameworks.
2. Testomat.io: Testomat.io is a user-friendly test management tool that helps teams plan, execute, and track their testing efforts. With customizable reports and support for test case design and execution, Testomat.io is a powerful tool for ensuring the success of your software projects.
3. TestLink: TestLink is a free test management tool that helps organizations improve the efficiency and effectiveness of their testing process. It includes features such as test case design and execution, defect tracking, and integration with bug tracking tools.
4. TestRails: TestRails is a feature-rich test management tool that helps teams plan, execute, and track their testing efforts. It includes features such as test case design and execution, defect tracking, and integration with agile development frameworks.

Conclusion

Test management is a vital component of the software development journey, one that ensures the end product is of the highest quality and reliability. It helps us catch defects early on, before they have a chance to cause issues down the line. Without a solid test management process in place, our software runs the risk of failing to meet user expectations and failing in its purpose. That’s why it’s so important to establish a robust test management process and stick to it every step of the way. And as the landscape of software development shifts and evolves, we must remain vigilant in our efforts to continuously review and improve our test process to meet the changing needs and challenges that come our way.

The post The Importance of Test Management in Software Development appeared first on testomat.io.

Test-driven development (TDD) and acceptance test-driven development (ATDD) are two popular approaches to software testing that are commonly used in the software development process. Both methods involve writing tests before writing code, but there are some key differences between the two approaches.

TDD is a software development process in which developers write tests for small units of code before writing the code itself. The goal of TDD is to ensure that the code being written is correct and meets the requirements specified in the tests. TDD typically involves writing tests at the unit level, which means that tests are written to verify the behavior of individual units of code.

ATDD, on the other hand, is a software development process in which developers write acceptance tests before writing code. Acceptance tests are designed to verify that the software meets the needs and requirements of the users. ATDD involves writing tests at the acceptance level, which means that tests are written to verify that the software performs as expected from the user’s perspective.

One key difference between TDD and ATDD is the level at which tests are written. TDD focuses on testing small units of code, while ATDD focuses on testing the overall functionality of the software from the user’s perspective. This means that ATDD tests are typically more high-level and involve testing the entire system, rather than just individual units of code.

TDD example in Python:

# Test for a simple function that adds two numbers
def test_add_two_numbers():
    assert add(2, 3) == 5
    assert add(-2, 3) == 1
    assert add(2, -3) == -1
    assert add(-2, -3) == -5

# Function to add two numbers
def add(a, b):
    return a + b

ATDD example in Python:

# Test to verify that the login feature is working as expected
def test_login():
    # Navigate to the login page
    driver.get('http://example.com/login')

    # Enter the username and password
    username_field = driver.find_element_by_id('username')
    password_field = driver.find_element_by_id('password')
    username_field.send_keys('testuser')
    password_field.send_keys('testpassword')

    # Click the login button
    login_button = driver.find_element_by_id('login_button')
    login_button.click()

    # Verify that the user is redirected to the dashboard page
    assert driver.current_url == 'http://example.com/dashboard'

Another difference between the two approaches is the focus of the tests. TDD tests are focused on verifying the behavior of individual units of code, while ATDD tests are focused on verifying that the software meets the needs and requirements of the users. This means that ATDD tests are often more complex and involve testing multiple units of code in combination.

In terms of the benefits of each approach, TDD is often seen as a way to ensure that code is correct and meets the specified requirements. It can also help developers identify problems early in the development process, which can save time and resources in the long run. ATDD, on the other hand, is often seen as a way to ensure that the software meets the needs and requirements of the users. It can also help developers understand the user’s perspective and build software that is more user-friendly.

TDD process

The complexity of implementing TDD is the need to continually write and run tests as the code is being developed. This requires a significant time investment and can be difficult to coordinate if the development team does not have a strong understanding of the coding process. It can also be challenging for development teams that do not understand coding to keep track of the various tests that have been written and ensure that they are all being run correctly.

The TDD process involves the following steps:

1. Write a test: The first step in the TDD process is to write a test for a small unit of code. This test should specify the behavior that the unit of code should exhibit. Example:

   # Test for a function that calculates the area of a triangle
   def test_calculate_area():
       assert calculate_area(3, 4, 5) == 6
       assert calculate_area(6, 8, 10) == 24
       assert calculate_area(12, 16, 20) == 96
   

2. Run the test: The next step is to run the test. At this point, the test should fail because the code being tested has not yet been implemented.
3. Write the code: After the test has been written and run, the next step is to write the code being tested. The code should be written in a way that meets the requirements specified in the test. Example:

   # Function to calculate the area of a triangle
   def calculate_area(a, b, c):
       s = (a + b + c) / 2
       return (s*(s-a)*(s-b)*(s-c)) ** 0.5
   

4. Run the test again: Once the code has been implemented, the test should be run again to verify that the code meets the requirements specified in the test.
5. Refactor the code: If the test passes, the code can be refactored to improve its design or structure without changing its functionality. Example:

   import math
   
   def calculate_area(a: float, b: float, c: float) -> float:
       s = (a + b + c) / 2
       area = s * (s - a) * (s - b) * (s - c)
       return math.sqrt(area)
   

   The refactored function uses the math.sqrt function to calculate the square root of the product, rather than using the exponentiation operator (**). This can be more efficient and easier to read.

   It’s important to note that the refactored code should not change the behavior of the function. It should still pass the tests that were written earlier in the TDD process.

6. Repeat the process: The TDD process is iterative, so the steps of writing a test, running the test, writing the code, and running the test again should be repeated for each unit of code being developed.

Overall, the TDD process involves a cycle of writing tests, writing code, and running tests to ensure that the code being developed is correct and meets the specified requirements. It is a way to ensure that code is correct and can help developers identify problems early in the development process.

TDD use cases and workflows

TDD approach is useful for ensuring that the software meets the needs and requirements of the users, as well as for facilitating collaboration among multiple parties involved in the development process. ATDD is often used in agile development, as it allows the development team to focus on small increments of functionality and quickly iterate on the software as the requirements evolve. It is particularly useful for customer-facing software, as it helps ensure that the software meets the needs and expectations of these users.

• Complex software with many requirements: TDD is well-suited for projects with a large number of requirements, as it helps ensure that all of the requirements are being met and that the software is functioning correctly.
• Agile development: TDD is often used in agile development, as it allows the development team to focus on small increments of functionality and quickly iterate on the software as the requirements evolve.
• Projects with frequent updates or changes: TDD helps ensure that the code remains stable and reliable despite frequent updates or changes.
• High-reliability software: TDD is particularly useful for software that requires a high level of reliability, such as software used in critical systems or safety-critical applications.

ATDD process

Implementing ATDD typically involves working with a variety of stakeholders, such as business analysts or product managers, who may have different perspectives on the software being developed. Coordinating with these stakeholders to ensure that the acceptance criteria and acceptance tests accurately reflect the needs and requirements of the users can be challenging.

The ATDD process involves the following steps:

1. Define acceptance criteria: The first step in the ATDD process is to define the acceptance criteria for the software being developed. This involves identifying the specific needs and requirements of the users and documenting them in the form of acceptance criteria. Example acceptance criteria:
   • The user should be able to search for any keyword using the Google search feature
   • The search results should be relevant to the keyword that was searched
   • The search results should be displayed in a clear and organized manner
2. Write acceptance tests: Once the acceptance criteria have been defined, the next step is to write acceptance tests. These are tests that are designed to verify that the software meets the acceptance criteria. Acceptance tests should be written in a way that is easily understandable by both developers and stakeholders, such as business analysts or product managers. Example acceptance test:

   # Test to verify that the Google search feature is working as expected
   def test_google_search():
       # Navigate to the Google search page
       driver.get('https://www.google.com')
   
       # Enter a keyword in the search field
       search_field = driver.find_element_by_name('q')
       search_field.send_keys('testomat.io')
   
       # Click the search button
       search_button = driver.find_element_by_name('btnK')
       search_button.click()
   
       # Verify that the search results are relevant to the keyword
       search_results = driver.find_elements_by_css_selector('.r')
       for result in search_results:
           assert 'testomat.io' in result.text.lower()
       
       # Verify that the search results are displayed in a clear and organized manner
       assert len(search_results) > 0
       assert driver.find_element_by_id('result-stats') is not None
   

3. Implement code: After the acceptance tests have been written, the development team can begin implementing the code. The code should be implemented in a way that meets the acceptance criteria and passes the acceptance tests. Some Python example:

   import requests
   
   class GoogleSearch:
       def __init__(self):
           self.results = []
   
       def search(self, keyword):
           # Send a GET request to the Google search API
           url = 'https://www.google.com/search?q={}'.format(keyword)
           response = requests.get(url)
   
           # Extract the search results from the response
           self.results = extract_results_from_response(response)
   
       def get_results(self):
           return self.results
   
       def get_result_count(self):
           return len(self.results)
   
   def extract_results_from_response(response):
       # Extract and return the search results from the response
       results = []
       # Add code here to parse the response and extract the search results
       return results
   

4. Run acceptance tests: As the code is being developed, the acceptance tests should be run regularly to ensure that the software is meeting the acceptance criteria. If a test fails, it indicates that the software is not meeting the acceptance criteria and needs to be modified.
5. Review and sign-off: Once all of the acceptance tests have been successfully passed, the software is ready for review and sign-off. This step involves reviewing the software to ensure that it meets the acceptance criteria and is ready for release.

Overall, the ATDD process involves a collaborative effort between developers, stakeholders, and other members of the development team to ensure that the software meets the needs and requirements of the users. It is an iterative process that involves regularly running acceptance tests and modifying the software as needed until all of the acceptance criteria have been met.

ATDD use cases and workflows

ATDD approach is useful for ensuring that the software meets the needs and requirements of the users, as well as for facilitating collaboration among multiple parties involved in the development process. ATDD is often used in agile development, as it allows the development team to focus on small increments of functionality and quickly iterate on the software as the requirements evolve. It is particularly useful for customer-facing software, as it helps ensure that the software meets the needs and expectations of these users.

• Complex software with many requirements: ATDD can help ensure that all of the requirements are being met and that the software is meeting the needs of the users.
• Collaborative development: ATDD involves collaboration between developers, stakeholders, and other members of the development team.
• Agile development: ATDD allows the development team to focus on small increments of functionality and quickly iterate on the software as the requirements evolve.
• Customer-facing software: ATDD is particularly useful for software that will be used by customers or end users, as it helps ensure that the software meets the needs and expectations of these users.

TDD vs ATDD Pros and Cons

Both TDD and ATDD can be useful approaches for software development, depending on the specific needs and constraints of the project. It is important to carefully evaluate the pros and cons of each approach and choose the one that best fits the needs of the project.

Pros of TDD:

• Helps catch errors and defects early in the development process, reducing the time and effort required to fix them
• Breaks the development process down into smaller, more manageable chunks
• Helps ensure that the code is correct and meets the requirements of the system

Cons of TDD:

• Can be time-consuming, as it requires writing and running tests before implementing new functionality
• Can be difficult to implement in situations where the requirements are complex or constantly changing

Pros of ATDD:

• Involves collaboration between developers, stakeholders, and other members of the development team
• Helps ensure that the software meets the needs and expectations of the users
• Allows for rapid iteration on the software as the requirements evolve

Cons of ATDD:

• Can be time-consuming, as it requires writing and running acceptance tests before implementing new functionality
• Can be difficult to implement in situations where the requirements are complex or constantly changing

Summary

Learning TDD and ATDD can be a challenging process, but it is well worth the effort. To get started, you might consider taking a course or workshop on the subject, or reading books or online articles to familiarize yourself with the concepts. As you continue to learn and practice TDD and ATDD, you will develop a deeper understanding of the approaches and how to apply them in your work. With time and practice, you will become proficient in these approaches and be able to use them to deliver high-quality software that meets the needs of your users.

To learn advanced tips and tricks for TDD and ATDD, you might consider the following:

1. Attend conferences or workshops on TDD and ATDD. These events often feature presentations and talks by experts in the field, as well as hands-on workshops and exercises to help you learn and practice the approaches.
2. Pair program with experienced TDD and ATDD practitioners. This can provide a valuable opportunity to learn from someone who has practical experience with the approaches, and can help you develop your skills and knowledge more quickly.
3. Practice, practice, practice! The more you work with TDD and ATDD, the more comfortable and proficient you will become. Consider setting aside time each week to practice the approaches on small projects or exercises, and don’t be afraid to ask for help or feedback from others as you learn.

Overall, both TDD and ATDD are valuable approaches to software testing that can help developers build high-quality software. The key is to choose the approach that best fits the needs of your project and your development team.

The post ATDD vs TDD: Understanding the Key Differences appeared first on testomat.io.

There are several benefits to using automated testing:

1. Improved accuracy: Automated tests can be run repeatedly, ensuring that the software is consistently performing as expected.
2. Increased efficiency: Automated tests can be run quickly and efficiently, allowing developers to test their code more frequently and catch issues early in the development process.
3. Reduced costs: Automated testing can save time and resources by eliminating the need for manual testing.
4. Improved reliability: Automated tests are less prone to human error, making them more reliable for verifying the correctness of software.

There are many different types of automated tests that can be used, including unit tests, integration tests, and acceptance tests. Each type of test has a specific purpose and is used at different stages in the development process.

The introduction to test automation process

1. Identify the goals of the testing process: Before starting the automated software testing process, it is important to determine the goals of the testing. This includes identifying the features and functionality that need to be tested, as well as the expected outcomes.
2. Identify the types of tests you need to run: Unit tests, integration tests, and/or acceptance tests. Choose the types of tests that are most appropriate for your software and its intended use.
3. Choose the right tools: There are a wide variety of tools available for automated testing, including open source and commercial options. It is important to choose the right tools for the project, taking into account factors such as the type of software being tested, the language it is written in, and the budget available.
4. Choose a testing framework: There are many different testing frameworks available that can be used to create and run automated tests. Some popular options include JUnit, TestNG, and Cucumber.
5. Write the test code: Use the testing framework and programming language of your choice to write the test code. Make sure to include assertions that verify the expected behavior of the software. Test cases should be written in a clear and concise manner, and should cover all the important functionality of the software.
6. Set up the testing environment: The testing environment should be set up to closely mimic the production environment in which the software will be used. This includes installing all necessary dependencies and configuring the test environment to match the production environment as closely as possible.
7. Run the tests: Use the testing framework or a continuous integration tool to execute the tests. The results of the tests will be reported, indicating which tests passed or failed.
8. Debug and fix any failing tests: If any tests fail, review the test code and the software to identify the cause of the failure. Make any necessary changes to the software or test code to fix the issue.
9. Analyze the results: After the tests have been run, it is important to analyze the results to determine if there are any issues that need to be addressed. If any defects are found, they should be logged and prioritized for fixing.
10. Repeat the process: As you continue to develop and modify the software, run the automated tests regularly to ensure that the software is functioning correctly. As changes are made to the software, new test cases should be written and the automated software testing process should be repeated to ensure that the changes have not introduced any new defects.

Skills necessary to make automated tests

In order to be effective, automated tests need to be well-designed, well-implemented, and well-maintained. So you need to know following:

1. Programming skills: Automated testing often involves writing code, so it is important to have a strong foundation in programming. This may include knowledge of languages such as Python, Java, or C#. Without strong programming skills, it may be difficult to design and implement automated tests in a way that is maintainable and scalable.
2. Familiarity with testing frameworks: There are various testing frameworks available, each with their own set of tools and features. It is important to have a good understanding of at least one testing framework and how to use it effectively.
3. Knowledge of testing principles: A tester should have a good understanding of testing principles, including how to design effective test cases, how to identify and prioritize defects, and how to track and report on testing progress.
4. Debugging skills: Automated testing can sometimes uncover defects that are difficult to reproduce or diagnose. It is important for a tester to have strong debugging skills in order to troubleshoot these issues.
5. Software design patterns: Familiarity with software design patterns can be helpful in understanding how to design and implement automated tests that are maintainable and scalable.
6. Software architecture principles: Software design patterns and software architecture principles can be useful in designing and implementing automated tests that are maintainable and scalable. A tester with knowledge of these concepts will be better able to design tests that are aligned with the overall architecture of the software.
7. Building infrastructure: Experience in building infrastructure, such as continuous integration and delivery systems, can be helpful in automating the testing process and ensuring that tests are run efficiently and effectively. Without this experience, it may be difficult to set up and maintain an automated testing process.

As a test automation engineer, there is generally a learning curve as you become familiar with the tools and techniques used in test automation. This may include learning programming languages like Java or Python to write test code, using testing frameworks like JUnit or TestNG to structure and run tests, applying test design patterns like the Page Object Model or Data-Driven pattern to organize test code, using test execution and reporting tools like Jenkins or Allure, and setting up and maintaining a test infrastructure to run tests. It may take some time to become proficient in these areas, but with practice and experience, you can develop the skills needed to effectively automate tests.

Test automation design patterns basics

A test automation design pattern is a standardized approach or strategy for designing and implementing automated tests. These patterns can help to ensure that the tests are maintainable, scalable, and reliable, and that they accurately reflect the desired behavior of the software. Test automation design patterns can be used to address specific challenges or problems that may arise during the automated software testing process, such as the need to test a wide range of scenarios, the need to create reusable test scripts, or the need to communicate the testing process to non-technical stakeholders. Some common test automation design patterns include the Page Object Model (POM), Data-Driven Testing, Keyword-Driven Testing, Behavior-Driven Development (BDD), and the Test Pyramid. By using test automation design patterns, testers can ensure that the automated testing process is efficient and effective, and that the software being tested is of the highest quality.

1. Page Object Model (POM): The Page Object Model is a design pattern that allows for the separation of the test code from the implementation details of the application under test. This makes it easier to maintain and update the tests as the application changes.To implement the Page Object Model, a page object class is created for each page in the application. This class contains methods that represent the actions that can be performed on the page, as well as any relevant elements on the page. In the test code, an instance of the page object class is created for each page that is needed. The methods on the page object instance are then called to perform the desired actions on the page. For example, if the page has a login form, the test code might call the “login” method on the page object instance, passing in the appropriate username and password.One of the benefits of the Page Object Model is that it allows for the test code to be updated independently of the application under test. If the application changes, the page object classes can be updated to reflect the changes, without the need to modify the test code. This makes the test code more maintainable and less prone to breaking when the application changes. The Page Object Model can also improve the scalability of the test code, as it allows for the creation of reusable test scripts that can be easily maintained and updated.Here is an example of the Page Object Model implemented in Python:

   # Page object class for the login page
   class LoginPage:
       def __init__(self, driver):
           self.driver = driver
           self.username_input = self.driver.find_element_by_id('username')
           self.password_input = self.driver.find_element_by_id('password')
           self.login_button = self.driver.find_element_by_css_selector('.login-button')
       
       def login(self, username, password):
           self.username_input.send_keys(username)
           self.password_input.send_keys(password)
           self.login_button.click()
   
   # Test code
   def test_login_functionality(driver):
       login_page = LoginPage(driver)
       login_page.login('user1', 'password')
       # Verify that the user is logged in
       assert driver.find_element_by_css_selector('.logout-button').is_displayed()
   

   In this example, the LoginPage class represents the login page of the application. It has methods for entering the username and password, and for clicking the login button. The test code creates an instance of the LoginPage class and calls the “login” method to log the user in. The test then verifies that the user is logged in by checking for the presence of the logout button. If the application changes, the LoginPage class can be updated to reflect the changes, without the need to modify the test code.

2. Data-Driven Testing: Data-Driven Testing is a design pattern in which the same test is run multiple times with different input data. This allows for the testing of a wide range of scenarios and can be useful in identifying defects that may not be apparent with a single set of input data.Here is an example of Data-Driven Testing implemented in Python:

   test_data = [    {'username': 'user1', 'password': 'password1', 'expected_result': 'Success'},    {'username': 'user2', 'password': 'password2', 'expected_result': 'Success'},    {'username': 'user3', 'password': 'invalid password', 'expected_result': 'Error'},]
   
   def test_login_functionality(driver):
       for data in test_data:
           login_page = LoginPage(driver)
           login_page.login(data['username'], data['password'])
           # Verify the result
           assert driver.find_element_by_css_selector('.result').text == data['expected_result']
   

   In this example, the test data is stored in a list of dictionaries, each representing a different scenario to be tested. The test code uses a “for” loop to iterate through the test data and execute the test for each scenario. The test code creates an instance of the LoginPage class and calls the “login” method to log the user in, and then verifies the result by checking the text of the element with the “result” class. By using Data-Driven Testing, the same test

3. Keyword-Driven Testing: Keyword-Driven Testing is a design pattern in which test cases are defined using a set of keywords that represent the actions to be performed in the test. This allows for the creation of reusable test scripts and can make it easier to maintain and update the tests.Here is an example of Keyword-Driven Testing implemented in Python:

   keywords = {
       'open_browser': lambda driver, url: driver.get(url),
       'input_username': lambda driver, username: driver.find_element_by_id('username').send_keys(username),
       'input_password': lambda driver, password: driver.find_element_by_id('password').send_keys(password),
       'click_login': lambda driver: driver.find_element_by_css_selector('.login-button').click(),
   }
   
   test_cases = [
       {
           "name": "successful login",
           "steps": [
               {"keyword": "open_browser", "args": ["http://www.example.com/login"]},
               {"keyword": "input_username", "args": ["user1"]},
               {"keyword": "input_password", "args": ["password1"]},
               {"keyword": "click_login"},
           ],
       }
   ]

   In this example, the keywords dictionary contains a set of functions that represent the actions that can be performed in the test. Each function takes a driver argument, which is the webdriver instance, and any additional arguments that are needed for the action.

   The test_cases list contains a set of dictionaries, each representing a different test case. Each test case has a name and a steps field. The steps field is a list of dictionaries, each representing a step in the test case. Each step has a keyword field, which is the name of the action to be performed, and an optional args field, which is a list of arguments to be passed to the action.

   To execute the tests, a loop is used to iterate through the test cases. For each test case, another loop is used to iterate through the steps. The keyword field is used to determine which action to perform, and the args field is used to pass the necessary arguments to the action. This allows for the creation of reusable test scripts that can be easily maintained and updated.

   Another example, the Robot Framework syntax is used to define the test cases using a keyword-driven approach. The test cases are implemented using the SeleniumLibrary, which provides a set of keywords for interacting with a web browser and performing actions such as opening a URL, entering text, and clicking a button.

   *** Settings ***
   Library           SeleniumLibrary
   
   *** Variables ***
   ${LOGIN_URL}      http://example.com/login
   ${USERNAME}       testuser
   ${PASSWORD}       testpass
   
   *** Test Cases ***
   Login Test
       Open Browser    ${LOGIN_URL}    chrome
       Input Text      id=username    ${USERNAME}
       Input Password  id=password    ${PASSWORD}
       Click Button    id=login
       Wait Until Page Contains Element    css=.welcome
       Close Browser
   

4. Behavior-Driven Development (BDD): BDD is a design pattern in which tests are written in a natural language syntax that is easy for non-technical stakeholders to understand. This can help to ensure that the tests accurately reflect the desired behavior of the software and can make it easier to communicate the automated software testing process to the development team.Here is an example of BDD implemented in Python using the Cucumber framework:

   Feature: Login functionality
     As a user
     I want to be able to log in to the application
     So that I can access restricted content
   
     Scenario: Successful login
       Given I am on the login page
       When I enter my username and password
       And I click the login button
       Then I should be logged in
   
     Scenario: Unsuccessful login
       Given I am on the login page
       When I enter my username and invalid password
       And I click the login button
       Then I should see an error message
   

   To implement the tests in Python, the scenarios can be written in Gherkin syntax and passed to Cucumber, which will generate the necessary test code. The test code can then be implemented using a tool like Selenium to interact with the application and verify the expected behavior.

   For example, the steps for the “Successful login” scenario might be implemented as follows:

   @given('I am on the login page')
   def open_login_page(context):
       context.driver.get('http://www.example.com/login')
   
   @when('I enter my username and password')
   def input_credentials(context):
       context.driver.find_element_by_id('username').send_keys('user1')
       context.driver.find_element_by_id('password').send_keys('password1')
   
   @when('I click the login button')
   def click_login_button(context):
       context.driver.find_element_by_css_selector('.login-button').click()
   
   @then('I should be logged in')
   def verify_login(context):
       assert context.driver.find_element_by_css_selector('.logout-button').is_displayed()
   

5. Test Pyramid: The Test Pyramid is a design pattern that recommends a balance between unit tests, integration tests, and end-to-end tests. It suggests that the majority of testing efforts should be focused on the lower levels of the testing hierarchy, with fewer tests at the higher levels. This approach can help to ensure that the tests are efficient and effective at identifying defects. Main parts of Test Pyramid:
   • Unit tests: These are tests that are focused on individual units of code, such as functions or methods. They are typically fast to execute and are used to validate the correctness of the code at a low level.
   • Integration tests: These are tests that verify the integration of different units of code, such as the interaction between different classes or modules. They are typically slower to execute than unit tests and are used to validate the correctness of the code at a higher level.
   • UI or End-to-End tests: These are tests that interact with the user interface of the application, such as through a web browser. They are used to validate the overall behavior of the application and can be time-consuming to execute.

   According to the Test Pyramid, there should be more unit tests than integration tests, and even fewer UI tests. This allows for a mix of fast-running, granular tests at the lower levels of the pyramid, with slower-running, broader tests at the higher levels.

   

Test automation infrastructure

Overall, building a technical test automation infrastructure requires careful planning and consideration of the resources and tools that will be needed to support the testing process. It’s important to ensure that the infrastructure is able to meet the needs of the development process and is able to support the testing efforts effectively.

1. Set up a testing infrastructure: This might include setting up servers or other infrastructure to run the tests, such as cloud servers or virtual machines. It’s important to ensure that the infrastructure is able to support the testing needs, including the number of tests that need to be run and the resources required to run them.There are several options for setting up a testing infrastructure to run tests. One option is to set up a physical server. This involves purchasing a physical server and installing the necessary operating system, test tools, and other software on it. The server can then be used to run the tests. It’s important to ensure that the server has enough resources, such as CPU, memory, and storage, to support the tests, and that the necessary test tools and software are installed.Another option is to set up a virtual machine. This can be done using a tool like VirtualBox or VMware, and allows you to create a virtual machine on a physical server or on a local machine. The virtual machine can then be used to run the tests. As with a physical server, it’s important to ensure that the virtual machine has enough resources to support the tests and that the necessary test tools and software are installed.A third option is to set up a cloud server. This involves using a cloud provider like Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform to set up a virtual machine in the cloud. The virtual machine can then be used to run the tests. One advantage of using a cloud server is that it can be more cost-effective than setting up a physical server, although it may require more maintenance to keep the infrastructure up to date. As with a physical or virtual machine, it’s important to ensure that the cloud server has enough resources to support the tests and that the necessary test tools and software are installed.Overall, it’s important to choose the testing infrastructure that best meets the needs of the project, considering factors such as cost, maintenance, and the resources required to run the tests.
2. Test runners: Test runners are tools that are used to execute tests and report the results. Some popular test runners include JUnit, TestNG, and PyTest.For example, JUnit might be used to execute a suite of unit tests for a Java application. The test code could be written using the JUnit framework, and JUnit could be used to run the tests and report the results.
3. Set up a continuous integration (CI) system: This might include setting up a system that automatically runs the tests as part of the development process. The CI system should be able to trigger the tests based on changes to the codebase, and should be able to provide alerts if any tests fail. Some popular CI tools include Jenkins, Travis CI, and CircleCI.For example, Jenkins might be used to automate the testing of a Java application. The test code could be configured to run whenever changes are made to the codebase, and the results of the tests could be reported automatically to the development team.
4. Set up a reporting system: This might include setting up a system to track the results of the tests and generate reports on their status. The reporting system should be able to track the results of individual tests, as well as the overall status of the test suite. Some popular test reporting tools include Allure, Extent Reports, and TestNG Reports.For example, Allure might be used to generate reports on the results of a set of functional tests for a web application. The test code could be run using a tool like Selenium, and the results could be reported to Allure along with any defects that were identified. The reports generated by Allure could then be used to track the progress of the tests and identify any issues.
5. Set up a test management system: This might include setting up a system to track the status of the tests and the defects that are identified during testing. The test management system should be able to track the progress of the tests, as well as the status of any defects that are identified. Some popular test management tools include TestRail, JIRA, and Testomat.io.For example, Testomat.io might be used to track the progress of a set of integration tests for a web application. The test code could be run using a tool like Selenium, and the results could be reported to Testomat.io along with any defects that were identified.

Roles and responsibilities in test automation

There are a number of roles that may be involved in the test automation process, depending on the size and complexity of the project. Here are some examples of roles that may be involved in test automation:

1. Test automation engineer: A test automation engineer is responsible for designing, implementing, and maintaining the test automation scripts. They may also be responsible for writing and executing test cases and analyzing test results.
2. Test lead: A test lead is responsible for overseeing the testing process and coordinating the work of the test automation engineers. They may be responsible for defining the automated testing strategy and overseeing the implementation of the test automation frameworks.
3. Test manager: A test manager is responsible for managing the testing process and the testing team. They may be responsible for defining the testing strategy, overseeing the implementation of the test automation frameworks, and managing the resources and budgets for the testing team.
4. Developer: Developers may be involved in the test automation process by writing code that integrates with the test automation frameworks, or by helping to identify areas of the codebase that may be prone to defects and need to be tested.
5. QA analysts: QA analysts may be involved in the test automation process by writing and executing manual tests, or by helping to design and implement the test automation descriptions.

Overall, the roles and responsibilities in the test automation process will depend on the specific needs of the project and the size and complexity of the testing team. Generally, responsibilities may be the following:

1. Design and implement the test automation strategy: This may involve defining the overall approach to test automation, including the tools and technologies to be used, the test cases to be automated, and the test data to be used.
2. Develop test automation frameworks: This may involve designing and implementing a test automation framework that can be used to run automated tests for the software. This may involve selecting the appropriate tools and technologies, defining the structure and organization of the test code, and integrating the test code with the software under test. The test automation framework may also include features such as test data management, test execution and reporting, and test maintenance.”
3. Write and execute test cases: This may involve writing test cases that exercise the various features and functionality of the software, and using the test automation tools to execute the tests.
4. Analyze test results: This may involve reviewing the results of the automated tests, identifying defects and failures, and reporting on the results to the relevant stakeholders.
5. Maintain the test automation frameworks: This may involve keeping the test automation frameworks up to date, debugging any issues that arise, and adding new test cases or functionality as needed.
6. Coordinate the work of the testing team: This may involve communicating with other members of the testing team, coordinating the execution of tests, and ensuring that all necessary resources are available.
7. Mentor and train junior team members: This may involve providing guidance and support to junior team members, helping them to develop their skills and knowledge in test automation.

Summary

In conclusion, automated testing is a valuable tool for ensuring the quality and reliability of software. As a test automation engineer, you have the opportunity to work with cutting-edge technology and be a part of a team that is working to deliver high-quality software to users. You’ll also have the opportunity to continuously learn and improve your skills, as the field of test automation is constantly evolving. Being a test automation engineer can be a challenging and rewarding career, and the skills you’ll develop will be valuable in many different industries. So if you’re passionate about software development and testing, consider pursuing a career as a test automation engineer – it’s a cool and exciting field to be a part of!

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