In fact, you must have two types of documents: a map of a test strategy and a test plan. Most QA teams mix these up, which causes problems throughout the software development process. When your testing team doesn’t understand the key differences between these documents, projects get messy, deadlines get missed, and overall quality suffers.

Let’s clear this up once and for all. Understanding what each document does – and how they work together – will make your QA process much more effective. Plus, with the right test management tool like Testomat.io, you can keep everything organized and running smoothly across your entire team.

What is a Test Strategy?

A test strategy is your big-picture guide for the overall testing approach. Think of it like the blueprint for a house – it shows the overall design and method, but doesn’t get into the detailed steps of which screws to use where.

What this test strategy document is all about is how your company or your testing team goes about working with quality assurance in general. Neither is it on a certain project. What it does instead is to discuss how you intend to manage the activities of testing on all your projects in the long run. The testing strategy answers key questions like:

• What type of testing do we do? (functional testing, performance testing, security testing, etc.)
• What test management platform and tools do we use?
• How do we measure if our testing efforts are working?
• Who among team members is responsible for what?
• What are our testing standards for software quality?

What Goes in a Test Strategy Document?

Your effective test strategy should cover these key components.

Who Creates the Test Strategy?

In most of the cases, the seniors draft and keep the test strategy drafts. This would cover QA leads, test managers, or other long-term team members who are aware of the technical aspect of software testing as well as the business side of it. These folks have the experience to make good decisions about the overall testing approach for the software testing process.

The testing strategy doesn’t change very often. Once you have an effective test strategy, you might update it yearly or when there are major changes to your software development process or project requirements.

What is a Test Plan?

Now let’s talk about test plans. If the test strategy is your blueprint, the test plan is your detailed document with specific instructions. It gets specific about how you’ll test one particular project or software release.

A test plan is a detailed guide that covers exactly what you’ll test, how you’ll test it, when you’ll do the testing tasks, and who will handle test execution. Unlike the test strategy, which stays pretty stable over time, you’ll create new test plans for each specific project or major release.
The test plan takes the big ideas from your test strategy and turns them into specific, actionable testing activities.

What Goes in a Test Plan?

Your comprehensive test plan should include these details.

Who Creates the Test Plan?

Test leads, team leads and project managers usually create test plans. These are people who understand the specific project requirements and can coordinate with all the team members involved in testing activities. They ensure effective communication between different parts of the QA teams.

Since test plans are project-specific, they get updated much more frequently than test strategies. You might revise your test plan several times during a single project as project requirements change or new information comes up during the software development process.

Key Differences Between Test Strategy and Test Plan

Let’s break down the key differences between these two documents that serve different purposes in your QA process.

How Test Strategy and Test Plan Work Together

These documents aren’t separate things that exist in isolation. They work together to create an effective testing process that supports software quality throughout the software development lifecycle.

Your test strategy provides the foundation for all testing efforts. It sets up the rules, standards, and overall testing approach that all your projects should follow. When it’s time to start a new specific project, you use your test strategy as the starting point for creating your detailed test plan.

This relationship improves the effectiveness of testing because you’re not starting from scratch with each project. Your strategy gives you a proven framework to build on. It also improves test coverage because your strategy ensures you’re thinking about all the important types of testing, not just the obvious ones.

Common Challenges and Best Practices

Many organizations struggle with keeping their test strategy and test plans effective throughout the software testing process. Here are the most common problems and how to avoid them:

Challenge 1: Mixing Up Strategy and Plan

A lot of QA teams create one document that tries to be both a strategy and a plan. This usually means they end up with something that’s either too vague to be useful as a detailed guide or too specific to work as an overall testing approach.

Solution: Keep them separate. Make sure your test strategy stays high-level and covers the overall testing approach, while your test plans get specific about testing tasks and detailed steps for each particular project.

Challenge 2: Poor Communication

Sometimes the people who write the test strategy don’t communicate well with the people who create test plans. This leads to plans that don’t align with the strategy, affecting the overall QA process.

Solution: Make sure your test management process includes regular communication between strategy and plan owners. Use a test management platform that helps everyone stay on the same page and supports effective communication.

Best Practices for Success

Keep your strategy stable but flexible – Your test strategy should provide consistent guidance over time for your overall testing approach, but it shouldn’t be so rigid that you can’t adapt to new situations in the software development lifecycle.

Make test plans structured and executable – Make your plans specific and actionable so that it provides the team members with specific details on when and what to do as far as testing is concerned.

Apply good tools – A good test management tool such as Testomat.io will keep everything in order and ensure your strategy and plans remain related during the testing.

Review regularly – Set up regular reviews for both your strategy and your plans. Strategy reviews might happen annually, while plan reviews might happen every few weeks during active projects to ensure they meet project requirements.

Get everyone involved – Include all relevant stakeholders when creating and reviewing these documents. This includes developers, testers, project managers, and business representatives to ensure effective communication and alignment with business goals.

Benefits for QA Teams Using Testomat.io

Teams that use Testomat.io for test strategy and plan management typically see several improvements in their software testing process:

Better organization – Everything related to the testing process is in one place, making it easier for team members to find what they need throughout the software development lifecycle.

Better communications – There is less confusion that surrounds the testing projects in terms of requirements, priorities, and testing progress when all the personnel are using the same test management application.

Better testing – The interdependence of the strategy, plans and execution also assists in having the testing efforts remain focused on the prioritized key test objectives and business objectives.

More convenient to follow – Colleagues who join an organization and should be trained on the QA process will find it easy to follow the process when documentation and organization of the process are done on Testomat.io.

Improved decision making – Managers can make better decisions when their reporting and measurements are good as they can decide on which testing should be prioritized and where resources should be directed to various projects.

Ready to Improve Your Test Management?

When your organization finds that it can no longer manage test strategies and test plans effectively for the entire process of software testing, perhaps a specialized test management tool can improve the results. Testomat.io is a platform that enables QA teams to approach their strategic, or big-picture planning as well as detailed instruction in a single platform.

With Testomat.io you may create and manage clear strategies of automated and manual tests, elaborate test plans to any particular project and make sure your daily testing efforts always contribute to the larger goals of your project. Platforms offer:

• Unified Test Management – Plan, run, and track manual and automated tests in a single, centralized platform.

• Collaboration Without Barriers – Share progress with developers, testers, and stakeholders in a format anyone can understand.
• AI-Powered Assistance – Auto-generate tests, receive improvement suggestions, and detect issues early.

• Flexible Test Execution – Target specific tests, switch environments instantly, and fine-tune execution settings.
• Unlimited Test Runs – Handle up to 100,000 tests in a single run without performance loss.
• Retrospective Change History – See what changed, when, and why with full test history tracking.

• Seamless Integrations – Works with Cypress, Playwright, Cucumber, WebdriverIO, Jest, and more.

Seamless Integrations

Ready to see how Testomat.io can help your testing team? Try the free trial and discover how much easier test management can be when you have the right test management platform supporting your process. Your QA teams and your software quality will thank you for it.

The post Test Strategy vs Test Plan: A Simple Guide for Better Software Testing appeared first on testomat.io.

The primary task of software testing undertaken by testing team members is to find and get rid of such deficiencies, employing specialized tools, and ensure the solution works properly and fulfills its assigned responsibilities up to the mark. All these procedures are implemented within a life cycle of bugs.

The article will explain bug life cycle in software testing, suggest a roster of test automation tools, describe bug life cycle stages, list the best practices of defect management, pinpoint the most frequent bad calls in bug tracking and handling, showcase the importance of bug life cycle management during the software development process, and give an example of a bug life cycle in a real-world situation.

What is Bug Life Cycle in Software Testing?

The bug life cycle (alternatively called a defect life cycle) is the journey of a defect from the first time it is detected to the final stage, which is its resolution. It is mission-critical for a developer team to go through life cycle stages early in the software development life cycle to address possible problems promptly and introduce the necessary code changes before defects become deeply embedded in the system.

Schematically, this process, namely Bug Lifecycle can be depicted as follows:

As an integral element of a broader software testing process aimed at ensuring optimal software quality, the bug life cycle plays a crucial role in it. Why? Because the software testing life cycle (STLC) provides only a general framework for versatile testing processes, whereas bug lifecycle software testing presents a detailed roadmap for managing individual defects that software testers reveal during the QA routine.

If you rely on the Agile methodology in SDLC, the software testing bug life cycle fits perfectly into it. The structured approach and dynamic nature that explain bug life cycle efficiency suit Agile practices to a tee, as both adhere to iterative and collaborative principles, allowing experts to exercise continuous improvement of the testing pipeline and reopen test cases if the root causes of issues are not removed.

To do their job well, testers can’t hope to grind it out only by manual testing. They can essentially streamline and accelerate the entire bug life cycle by leveraging the right tools and automation platforms.

Zooming in on Defect Tracking Tools

What is bug life cycle without robust tools? A tedious and long toil subject to mistakes and other human-factor shortcomings. We offer a shortlist of top-notch tools that can help you accelerate and simplify the routine.

• Testomat.io. A cost-efficient tool that plays well with Jira and enables QA teams to convert manual tests into automated, attach screenshots and videos to inspect failing tests, peruse test analytics, and rerun failed tests. Tools listed below are also great, but you don’t need to use them separately since Testomat.io allows full integration with all of them.
• Jira. Created by Atlassian, the Jira bug life cycle platform integrates seamlessly with numerous third-party tools (including TestRail for QA collaboration) and provides issue tracking via customizable workflows, as well as advanced analytics and reporting. Besides, managing the Agile-driven bug life cycle in Jira is a breeze with specialized boards for Kanban and Scrum.
• Linear. A very solid open-source testing bug life cycle tool. It is simple to use, offers comprehensive charting and bug report opportunities, and enjoys a wide community support. The platform sends notifications to keep teams in the know concerning bug status changes and exercises access control that safeguards secure collaboration.
• Azure Devops. Probably the best free option on the market, with a user-friendly UI, customizable workflows, email notifications, and time tracking allowing for effective resource management. Plus, you can augment the functionality of this bug life cycle testing tool by installing plugins.
• GitHub Issues. It is a versatile cloud-driven platform that comes as part of any GitHub source code repository. Its functionalities go beyond tracking the life cycle of a bug in software testing and monitoring the defect status via progress indicators at different stages of the bug life cycle. GitHub Issues can also be used for visualizing large projects in the form of tables, boards, charts, or roadmaps, automating code creation workflows, hosting discussions, handling internal customer support requests, submitting documentation feedback, and more.

Although efficiently managing the life cycle of a bug without automation tools is next to impossible, a vetted software developer can’t rely solely on them. Why? Because automated tests not only detect bugs but also create them on the fly. In such cases, experts must step in to analyze it, understand the cause, and produce a detailed report.

That is why you should integrate both automated and manual techniques to understand the different states of the system better and improve bug triage.

While automated testing allows for efficient establishment of CI/CD pipelines and building bug feedback loops by handling repetitive and high-volume checks, managing the bug life cycle in manual testing provides in-depth, human-centric insights and offers flexibility in examining complex scenarios.
It is impossible to explain bug life cycle in testing without considering various stages of the process.

Stages of the Bug Life Cycle Dissected

The procedure of detecting and resolving bugs involves several stages. Let’s enumerate them.

1⃣ New

When a new defect is registered for the first time, it is assigned a “NEW” status.

The tester logs a detailed report on it via an issue tracking or test management tool, auto-linking it to tests and requirements. As a result, the development team can easily find it in the document and deal with it.

2⃣ Assigned

After the bug is logged, the lead/test manager reviews it and assigns it to a developer for resolution.

3⃣ Open

The developer starts analyzing the defect and resolving it. If the bug is found inappropriate, it acquires either the “Deferred” or “Rejected” state.

4⃣ Fixed/In Progress

After introducing relevant code changes and verifying them, the developer eliminates the bug and assigns it the “Fixed” status, thus informing the development lead that it is ready for retesting.

5⃣ Test/Retest

Depending on the context and the nature of the bug, the QA team employs either exploratory or regression testing to verify bug fixes.

6⃣ Verified

This stage of the bug life cycle confirms that the defect has been eliminated and is no longer reproduced in the environment.

7⃣ Closed

The resolved bug is assigned a “Closed” status by the QA engineers once it disappears from the system after its testing.

8⃣ Reopen

This is an optional status for bugs assigned to them if they reappear during retesting or at any other stage. In such cases, the bug life cycle is repeated until the issue is resolved.

9⃣ Deferred or Rejected

These are also optional. The “Deferred” status is assigned to real bugs that are not urgent and are expected to be handled in a future release. The “Rejected” status signals that it is not a defect or that it is the same bug registered again by mistake.

While performing software bug life cycle management across all the stages, the project manager can track the efficiency of the procedure through analytics and reporting capabilities provided by testing platforms. It can be done by monitoring defect coverage, defect density, mean time to resolution (MTTR), test execution time, defect removal efficiency, and other metrics.

You can’t answer the question “What is bug life cycle in testing?” correctly without understanding the differences between such terms as bug status, bug priority, and bug severity.

Distinguishing Bug Status vs. Bug Priority vs. Bug Severity

Among these three notions, the bug status is the most distinct. As we have seen above, it reflects the current stage of the bug resolution pipeline (new, open, in progress, verified, closed, etc.). The difference between the other two is more of a poser.

Bug severity is a parameter that gauges the technical impact of a defect on the system’s functionality. Its levels (trivial, minor, moderate, major, or critical) are determined by the QA team and represent the degree of such an impact, indicating how much the product’s behavior suffers.

For instance, if a bug causes the solution to crash, it is considered critical, whereas simple typos might be deemed minor or even trivial.

Bug priority (typically determined by project or product managers who are aware of business requirements) manifests how urgently you should fix the bug. Priority levels are categorized into high, medium, and low, where, say, a logging-preventing defect is considered a high-priority one, while a bug affecting UI rendering on certain operating systems is assigned a low-priority status.

Basically, severity is more technical and thus objective and consistent across organizations, whereas priority is business-driven (and consequently subjective) and can vary in regard to user impact and business needs.

Let’s illustrate what is bug life cycle with example of an imaginary e-commerce site, where defects are assigned a certain status, severity, and priority.

When setting up a defect life cycle pipeline, it is vital to log each bug properly.

How to Log a Bug Effectively: Key Guidelines

The best practices of logging bugs include:

• Clear title. The bug’s description should be unambiguous and concise, focusing on the specific problem.
• Steps to reproduce. By indicating precise steps for bug reproduction, you will enable the QA team to easily fix and verify the defect.
• Expected vs actual results. You should specify what you expected to achieve and what happened in fact. Seeing the discrepancy, testers can understand the bug’s nature and figure out how to fix it.
• Environment details. Indicate the hardware, browser, operating system, and any other relevant information concerning the IT environment.
• Screenshots/videos/logs. Visual aids provided by testing tools are a second-to-none means of showcasing the bug and its impact. For instance, Testomat.io’s rich context attachments improve bug life cycle clarity and allow developers to understand the problem in no time.

All these details, as well as the indication of the bug’s status, severity, and priority, are entered into the bug report.

However, even the most perfect report doesn’t protect you from mistakes in the process of bug fixing.

Common Bug Handling Mistakes to Avoid

QA greenhorns often botch the bug resolution routine through some typical bad calls.

• Incomplete or vague reports. The substandard report quality can have a negative impact on the entire process, turning bug reproducing and fixing into a tall order for developers.
• Duplicated bugs. When testers have to deal with the same defect reported multiple times, it dramatically slows down the life cycle.
• Improper status transition. Forgetting to change the bug life cycle status in the log may result in reopening previously closed cases again (and thus wasting time) or overlooking unfixed bugs that were erroneously marked as closed.
• Communication breakdowns. Information silos and miscommunication between the testing and developer personnel cause delays, incomplete bug fixing, and general frictions within the organization.

To better understand how it works, let’s examine the bug life cycle in action.

A Real-World Bug Life Cycle Use Case

We will showcase a bug life cycle using Testomat.io in combination with GitHub Issues and Playwright.

Why Bug Life Cycle Management Matters

As a vetted vendor offering robust testing tools, we understand that the outcome of the testing process is conditioned not only by the leveraged tools but also by the well-established bug life cycle. When properly set up and implemented, it brings the following perks.

• Faster resolution. By prioritizing bugs and effectively resolving them, QA teams minimize delays in the product’s time-to-market and ensure the solution’s timely delivery.
• Improved collaboration. Thanks to the structured bug life cycle, developers, testers, and other stakeholders have transparent communication guidelines in place, fostering unified effort in issue resolution.
• Enhanced product quality. A well-defined life cycle ensures a systematic approach to bug fixing, thus guaranteeing that all issues are detected and addressed, and a high-quality software product enters the market.
• Support for Agile/DevOps processes. A thorough bug life cycle is bedrock for Agile and DevOps methodologies. It not only enables prompt and efficient bug fixing but also establishes a collaborative culture of continuous improvement of testing and development workflows and promotes quality-centered software building.

Conclusion

The bug life cycle is a clear-cut path that describes the journey of a software defect from detection to resolution. Typically, it moves through several basic stages where the bug status changes (new, assigned, open, fixed, test, verified, closed). When logging a bug, you should enter its title, steps necessary for defect reproduction, environment details, expected and actual results, bug resolution priority and severity, and add relevant screenshots or videos.

An efficient bug life cycle management is impossible without robust automation tools. We highly recommend Testomat.io – a first-rate testing platform whose unquestionable fortes are excellent test process visibility, real-time bug management, and numerous third-party integrations. Contact us to try Testomat.io!

The post Bug Life Cycle in Software Testing: Stages, Tools & Real-World Examples appeared first on testomat.io.

In the Software Development Life Cycle (SDLC), manual testing plays a critical role in validating business logic, design flow, usability, and performance before the product reaches users. While automation testing has become increasingly popular, manual testing remains essential in areas where human intuition, flexibility, and context are required.

Why Manual Testing Still Matters

Despite the rise of test automation and the fact that manual testing is the most time-consuming activity within a testing cycle according to recent software testing statistics, 35% of companies identify it as their most resource-intensive testing activity. Manual testing is still very much relevant since this investment of time and human resources pays dividends in software quality and user satisfaction.

1⃣ Human Intuition VS Automation

Automated tools follow predefined scripts, unless they use AI. They can not anticipate unexpected user behavior or detect subtle design flaws. Human testers can apply empathy, common sense, and critical thinking, all key to evaluating user expectations and user satisfaction.

2⃣ Usability & Exploratory Testing

During exploratory testing, testers navigate the software freely without predefined scripts. This helps uncover hidden bugs and usability issues that structured testing might miss. It’s especially useful in early development stages when documentation is limited or evolving.

Exploratory testing, a key type of testing performed manually, allows testers to investigate software applications without predefined test scripts. This testing approach encourages testers to use their creativity and domain expertise to discover edge cases and unexpected behaviors that scripted tests might overlook.

3⃣ Edge Cases That Automation May Miss

Many edge cases, like odd screen resolutions, specific input combinations, or unusual user flows, are too complex or infrequent to automate. Manual testing ensures comprehensive coverage of these irregular scenarios.

4⃣ Early-Stage Product Testing

When a product is still in the concept or prototype phase, test cases evolve rapidly. Manual testing is more adaptable in such fluid environments compared to rigid automation scripts.

5⃣ Compliance, Accessibility, and Visual Validation

Testing for accessibility standards, compliance with legal requirements, and visual/UI validation often requires a human touch. Screen readers, color contrast, font legibility, and user interface alignment can’t be reliably assessed by machines alone.

Key Components of Manual Testing

Test Plan

A test plan is a high-level document that outlines the testing approach, scope, resources, timeline, and deliverables. It is a roadmap that guides testers and aligns them with the broader goals of the development team.

The test plan coordinates testing activities across the development team and provides stakeholders with visibility into testing efforts. It typically includes risk assessment, resource allocation, and contingency plans for various scenarios that might arise during test execution.

Test Case

A test case is a set of actions, inputs, and expected results designed to validate a specific function. A well-written test case includes:

• Test ID
• Title/Objective
• Steps to reproduce
• Expected results
• Actual results
• Pass/Fail status

Effective test cases are clear, concise, and reusable across different testing cycles. They should be designed to verify specific functionality while being maintainable as the software evolves through the development process.

Test Scenario vs. Test Case

While often confused, test scenarios and test cases serve different purposes in the testing process.

• Test Scenario: A high-level description of a feature or functionality to be tested.
• Test Case: A detailed checklist of steps to validate the scenario.

Scenarios help testers understand what to test; cases define how to test it.

Manual Test Execution

Manual test execution is the phase where testers manually run each test case step-by-step without using automation tools. It involves simulating real user actions, like clicking buttons, entering data, or navigating pages to verify that the software behaves as expected.

Bug Report

A clear bug report should contain:

• Summary
• Steps to reproduce
• Expected vs. actual result
• Screenshots or videos
• Severity and priority
• Environment details

Good reporting accelerates bug resolution and fosters collaboration across teams.

Test Closure

A test environment replicates the production environment where the software will run. It includes:

• Operating systems
• Browsers/devices
• Databases
• Network conditions

Testing on real devices in a well-configured environment ensures reliability.

Step-by-Step: Manual Testing Process

Manual testing follows a structured yet flexible flow.

1⃣ Requirement Analysis

The manual testing process begins with thorough requirement analysis, where testers review functional specifications, user stories, and acceptance criteria to understand what needs to be tested. This phase involves identifying testable requirements, clarifying ambiguities with stakeholders, and understanding the expected behavior of the software application.

During requirement analysis, testers also identify potential risks, dependencies, and constraints that might impact the testing approach. This analysis forms the foundation for all subsequent testing activities and helps ensure that testing efforts align with business objectives.

2⃣ Test Planning

Test planning involves creating a comprehensive strategy for the testing effort, including defining the testing scope, approach, resources, and timeline. This phase results in a detailed test plan that guides the entire testing process and ensures that all stakeholders understand their roles and responsibilities.

Effective test planning considers various factors such as project constraints, available resources, risk levels, and quality objectives. The plan should be detailed enough to provide clear guidance while remaining flexible enough to adapt to changing requirements.

3⃣ Test Case Design

Test case design transforms requirements and test scenarios into executable test procedures. This phase involves creating detailed test cases that cover both positive and negative scenarios, edge cases, and boundary conditions. Test case design requires careful consideration of test data requirements, expected results, and traceability to requirements.

Well-designed test cases should provide comprehensive coverage while remaining maintainable and efficient to execute. The design process often involves peer reviews to ensure quality and completeness of the test cases.

4⃣ Test Environment Setup

Setting up the test environment involves configuring all necessary infrastructure, installing required software, preparing test data, and ensuring that the environment closely resembles the production setting. This phase is critical for obtaining reliable and meaningful test results.

Environment setup also includes establishing processes for environment maintenance, data refresh, and configuration management. Proper environment management helps prevent testing delays and ensures consistent test execution.

5⃣ Test Execution

Test execution is where testers actually run the test cases, compare actual results with expected outcomes, and document any deviations or defects. This phase requires careful attention to detail and systematic documentation of all testing activities.

During test execution, testers may also perform ad-hoc testing and exploratory testing to investigate areas not covered by formal test cases. This combination of structured and unstructured testing helps maximize defect detection.

6⃣ Defect Reporting and Tracking

When defects are discovered during test execution, they must be documented, classified, and tracked through to resolution. This phase involves creating detailed bug reports, working with developers to clarify issues, and verifying fixes when they become available.

Effective defect management includes categorizing bugs by severity and priority, tracking resolution progress, and maintaining metrics on defect trends and resolution times.

7⃣ Test Closure Activities

Test closure involves completing final documentation, analyzing testing metrics, conducting lessons learned sessions, and archiving test artifacts. This phase ensures that testing knowledge is preserved and that insights from the current project can inform future testing efforts.

Test closure activities also include final reporting to stakeholders, confirming that exit criteria have been met, and transitioning any ongoing maintenance activities to appropriate teams.

What are The Main Manual Testing Types?

Manual testing covers various types of testing, including:

• Smoke Testing: Quick checks to ensure basic functionality.
• Sanity Testing: Verifying new functionality or bug fixes.
• Regression Testing: Ensuring code changes haven’t broken existing features.
• Exploratory Testing: Tester-driven, creative exploration.
• Ad-hoc Testing: Informal testing without documentation.
• Usability Testing: Assessing user experience and user interactions.
• User Acceptance Testing (UAT): Final checks by actual end users.

These types are essential for verifying software applications from multiple angles.

Manual vs Automated Testing: When to Use Each

The choice between manual and automated testing depends on various factors including project timeline, budget, application stability, and testing objectives. The adoption of test automation is accelerating, with 26% of teams replacing up to 50% of their manual testing efforts and 20% replacing 75% or more.

Criteria	Manual Testing	Automated Testing
Best For	UI, exploratory, short-term	Repetitive, regression, load, performance
Speed	Slower	Faster
Human Insight	Yes	Limited
Cost	Lower up front	High setup, low long-term cost
Tools	Basic (Google Docs, Jira)	Advanced (Selenium, Cypress)
Scalability	Limited	High
Reusability	Low	High

What are The Manual Testing Tools That You Should Know?

Even manual testers rely on tools to streamline the process:

• Test Case Management: Testomat.io, TestRail, TestLink
• Bug Tracking: Jira, Bugzilla
• Documentation: Confluence, Google Docs
• Screen Capture/Recording: Loom, Lightshot
• Spreadsheets & Checklists: Excel, Notion

These tools enhance collaboration, track progress, and improve test management.

Manual & Automation Test Synchronization

Modern QA practices combine both methods. For example:

• Start with manual testing in early phases
• Automate repetitive testing tasks later (like regression testing)
• Sync manual and automated test scripts in one platform (e.g., Testomat.io)
• Use manual results to refine automated test cases

This hybrid model ensures flexibility, scalability, and comprehensive coverage across all aspects of testing.

Challenges in Manual Testing

Manual testing isn’t without its pain points.

That’s why many organizations transition to a blended approach over time.

Best Practices for Manual Testers

If you’re just starting or looking to improve your testing approach, you can use these strategies. After all, a good manual tester is curious, methodical, and collaborative.

Keep Test Cases Clear and Reusable

Clarity beats cleverness. Well-written test cases should be easy to follow, even for someone new to the project. Reusability reduces maintenance and makes each testing cycle more efficient.

Tip: Use plain language, avoid jargon, and focus on user behavior. Think like an end user.

Use Checklists for Repetitive Tasks

For things like test environment setup or basic UI validation, checklists reduce mental load and human error. They’re your safety net — and they evolve as your app does.

Tip: Maintain checklists for app testing, integration testing, and performance testing workflows.

Collaborate With Developers and Designers

The closer QA is to the development team, the faster bugs are fixed — and the fewer misunderstandings happen. Collaboration leads to better alignment on user experience, design intent, and edge cases.

Tip: Attend sprint planning and design reviews to catch issues early and align on testing expectations.

Log Bugs Clearly With Repro Steps

A bug report should speak for itself. Vague or incomplete reports only delay fixes. Include reproduction steps, browser/device info, and screenshots or screen recordings when possible.

Tip: Use structured bug templates and emphasize test environment details and internal structure concerns (e.g., API responses or backend logs).

Learn Basic Automation for Hybrid Roles

Even if you’re focused on manual QA, learning the basics of test automation makes you more flexible and future-ready. It also helps you write better test cases that support both manual and automated testing pipelines.

Tip: Start with tool like Cypress, and learn how automation tools complement manual techniques.

Conclusion

Manual testing is far from obsolete. It remains a cornerstone of software quality assurance, especially when human judgment, context, and creativity are needed. It allows teams to evaluate user experience, uncover subtle bugs, and validate features in real-world scenarios. As products evolve, combining manual and automation testing provides the best of both worlds.

Fortunately, now there is Testomat.io, which can help you manage automated and manual testing in one AI-powered workspace, connecting BA, Dev, QA, and every non-tech stakeholder into a single loop to boost quality and faster delivery speed with AI agents. Contact our team now to learn more about Testomat.io.

The post What is Manual Testing? appeared first on testomat.io.

Thus, poorly developed software products can ruin the user experience and the company’s name and reputation.

The new trend of applying AI agents in testing can minimize human errors, make the QA teams more productive, and dramatically increase test coverage. In the article below, you can find out what AI-based agent testing is, discover types of AI agents in software testing and their core components, learn how to test with AI agents, and much more.

What is an AI Agent?

An artificial intelligence agent, or AI agent for short, is both a small program and a complex system which has been programmed to utilize artificial intelligence technology for completing tasks and meeting user needs. Thanks to its ability to demonstrate logical reasoning and understand context, an AI-backed assistant can make decisions, learn, and respond to changes on the fly. In most cases, AI agents or AI-powered assistants are characterized by the following:

• They can carry out repetitive or expert tasks and can even replace the whole QA team department.
• They can function autonomously when there is a need to attain defined goals (often without constant human intervention).
• They can be fully integrated into organizational workflows.

What is an AI Agent Testing?

When we talk about AI testing agents or assistants, we mean smart systems that apply artificial intelligence (AI) and machine learning (ML) to perform or assist in software testing tasks.

They replicate the work of human testers, including test creation, execution, and maintenance, with limited manual involvement of the specialists, operating under specific parameters they define. It is helpful to have AI-powered assistants in the following situations:

• With their help, anyone in the team, even without technical expertise, can create and maintain stable test scripts in plain English.
• They can automatically adjust, fix, and update tests in terms of the system changes, requiring less effort from human testers.
• Suggest how to make your tests better.
• They can automatically run test cases, which have been created with manual effort, and require minimal direct control of the QA specialists.

Types of AI Agents in Software Testing

Below, you can find information about widely known categories of AI agents in software testing, based on their roles and capabilities:

Simple Reflex QA Agent

Reflex Agents use if-then rules or pattern recognition. They follow specified instructions or fixed parameters with rule-based logic and base their decisions on current information. As the most basic type, these AI agents perform tasks based on direct responses to environmental conditions, which include diverse OSs and web browsers, network connections, structured and unstructured data formats, poorly documented APIs, and user traffic. For example, a task for a simple reflex agent can be to detect basic failures (e.g., 404s, missing elements), log errors, and take screenshots when it detects an error message on the screen.

Model-based Reflex Test Agent

These agents are intelligent enough to act on new data, not just directly, but with an understanding of the context in which it is presented. They are considering the broader context and then responding to new inputs. Can simulate user flows or business logic. Reflex Agents are used in testing to perform more complex testing tasks, because their decisions are based on what they remember/know about the situation around them.

For example, this type of agent remembers past login attempts. If users make multiple failures, it will try to attempt a Forgot Password process or alert about a potential account lockout instead of just logging an error.

Goal-based Agents

When we talk about this type of agent, we also mean rule-based agents. It happens because they follow a set of rules to achieve their concrete goals. They choose the best tactics or strategy and can use search and planning algorithms, which help them achieve what they want. For example, if an agent’s goal is to find every unique error or warning, it can be done by creating test scripts, which will help identify only unforeseen errors and decrease re-testing efforts.

Utility-based AI testing Agent

These agents are capable of making informed decisions. They can analyze complex issues and select the most effective options for the actions. When predicting what might happen for each action, they rate how good or useful each possible outcome is and finally choose the action most likely to give the best result.

For example, instead of just running all tests like Goal-based agents or reacting to immediate errors like simple reflex agents, these ones include a utility function, which allows them to rate situations and skip some less critical ones to find high-severity bugs.

Learning Agents

These assistants can use past experiences and learn from past mistakes or code updates. Based on feedback and data, they can adapt and improve themselves over time. For example, QA teams can use learning agents if there is a need to optimize regression testing. AI-powered assistants self-learn over time from previous bugs and codebase changes, prioritising areas with frequent failures — they highlight the team’s attention on what matters most.

The core components of AI agent for QA Testing

The essential parts of an AI agent for QA testing allow it to intelligently analyse, learn, adapt, and act during the software testing process. Let’s explore them more:

• Perception (Input Layer). Collects data from the environment. It might be code changes, test results, test execution logs, test diff analysis, API responses or some patterns in the test project.
• Knowledge Base. Stores the historical information the agent learns from. Its contents typically include past bugs and their root causes, test coverage data, and frequently failing components, e.g., this helps the Testing Agent make informed decisions based on context and experience.
• Reasoning Engine (AI agent brain). Makes decisions based on current input and the knowledge scope. AI agents use techniques such as rule-based logic, impact analysis, risk-based prioritization, and dependency graph evaluation to process information. For example, the agent can decide which tests to execute based on recent code changes.
• Learning Module. AI agents learn from user stories and test cases by training, adapting to patterns in them over time. This continuous learning helps reduce false positives and minimize test noise, improving overall test reliability. By leveraging natural language processing (NLP), they can predict potential test failures, detect flakiness and intelligently optimize the sequence in which tests are executed.
• Action(Execution Engine). Performs tasks based on decisions. For example: generating new test cases, selecting and proposing the execution of the most relevant tests, reporting defects or opening issues.
• Feedback Loop. Analyzes test engineers’ feedback on false positives to improve future answers. It enables continuous learning and self-improvement of the AI agent.
• Integration Layer. This layer enables seamless connection between the agent and external tools, allowing it to both send and receive data. The incoming information supports the agent’s perception and learning modules, enhancing its ability to respond in a reasoned and intelligent manner throughout the testing process. Integrates the agent with test frameworks, bug trackers, project management tools like Jira, documentation extensions such as Confluence and CI\CD pipelines.

So, these components work together to support automated, efficient, and smarter testing.

Considering this flow… How do you think? Where should test engineers focus the most? Right! It is their prompting and the filling of artifacts and documentation. Moving forward with our next paragraph:

What kind of feeding AI agent is dependent?

First, focus on the efficiency of your inputs — specifically, the prompts— since the accurate and useful results you get will directly depend on how your instructions are formulated. Today, prompting has become a key skill for modern QA engineers working alongside AI-driven tools. Thus,

What is prompt engineering in software testing?

It is your specific questions, instructions, and inputs given to the MCP AI agents to guide their responses; they can generate and improve tests, highlight potential faults, assist with analysis, and decision-making.

5 basic rules for prompting in software testing

1. Be clear, avoid vague instructions. Define exactly what you want: test type, tool, scenario, outcome.

Example of right prompt to generate test cases:

 Generate test cases for the login form with email and password fields and a button Sign In to verify their work.
 Instead of just: Write a test for the form.

2. Provide Context. The more context, the better the result. Include the user story, functionality, code snippet, or bug description.

Example a prompt based on user story:

Based on this user story <User Story RM-1523>: As a user, I want to reset my password via email, suggest edge cases for testing.
Generate tests for password.

3. Include expected behavior or test goals. Helps AI understand the validation points or test criteria. Also, use terms like: edge cases, negative testing or happy path, e.g., to guide the logic.

Example:

Write a test case that ensures users are redirected to the dashboard after logging in.”

4. Avoid overloading the prompt. Do not cram too many instructions in one sentence. It may confuse the MCP Agent’s model and reduce output quality. Better break large tasks into smaller steps, using step-by-step or follow-up prompts.

Example of step-by-step prompt

Start with: “What should I test in…” then ask for detailed test cases or code.

5. Mention tool, framework and format. If you are using a specific stack (like Playwright, Cypress, Selenium), need output checklist code to describe a test case with Gherkin, etc., just say it in the prompt.

Examples:

Write Gherkin-style scenarios for login functionality in Playwright with invalid credentials.
Return the test cases in a markdown table format.

Reasonable artifacts in input data equal efficient AI prompting

Secondly, always remember to keep your artifacts well-structured. Avoid inflating your test project with excessive, unused test cases — that’s a red flag. Prioritize only what’s necessary. This discipline is especially important for AI agents, as they rely on clear, relevant artefacts and operate them in the following:

• Requirements & Specifications. AI agents should have access to a detailed description of the intended purpose and environment for the system under testing. Knowing functional and non-functional requirements allows assistants to better understand what the system should do and how well it should perform in terms of speed, usability, security, etc.
• Existing user stories. AI agents need access to users’ stories to investigate the desired features from a user’s perspective, which allows them to apply them to simulate realistic user journeys and test the end-to-end experience.
• Test Cases. AI-based agents need access to test cases because these give them an understanding of what steps to take and what situations to test when checking software. With this information, assistants can make sure that the software works correctly and can find any bugs.
• Bug Reports. You can find them in Jira, Bugzilla, Linear, or some internal analytics dashboards, as well as external tools like testomat.io Defect Tracking Metrics. AI agents use linking with them for bug reproduction, identifying the fault reason in runs. AI can summarize bug trends to inform QA decision-making in bug prevention.
• Reporting and Analytics metrics. AI agents collect data from tools like Allure reports, CI/CD pipelines, and test dashboards. The AI evaluates test duration, failure trends, and pass/fail consistency. Frequent or critical test failures are flagged for priority investigation. AI agent provides suggestions for fixing unstable tests. Also learns which tests are most valuable for regression based on historical value. Based on these insights, it recommends test automation optimization.
• Documentation. With access to the testing documents, AI agents are in the know of what the software should do and what its goals are. These documents also tell them exactly what to test, give clear rules, and expected results – passed or failed tests.  Also, AI-based agents can run existing tests and learn from past results in reports to carry out smarter and more effective testing.

Choose the best Artificial Intelligence testing agent

The test management system testomat.io  is a modern AI-powered test management tool that helps you easily develop test artifacts and organize test projects with maximum clarity.
It is more than just a data repository and a tool for storing your test artifacts; it offers powerful, AI-driven functionality to accelerate your QA process. The AI Orchestration is integrated across your entire test lifecycle — from requirements to execution and defect tracking — while synchronising automation and manual testing efforts, supported by numerous integrations and AI enhancements.

Other strengths are Collaboration and Scalability. QAs in the team can easily share their test result reviews, flexibly select tests for test plans and runs, adapting AI suggestions to fit their specific needs.

AI Assistant testing works at the level:

Generative AI and Chat with test modes provide direct interaction with the test case suites using natural language — it looks like chatting with a QA teammate. You can generate new test cases or refactor existing ones by automating these repetitive test tasks; manage suggestions, map tests to requirements, identify test gaps and flaky scenarios, and gain a clearer understanding of your test coverage to improve it continuously.

For instance, prompt samples of questions:

→ What does this test case do?
→ Write edge tests for password reset
→ Rewrite this test to be more readable
→ Which parts of the app are under-tested?
→ Find duplicates
→ Map these test cases to requirements
→ To improve desired feedback, teach the MCP AI model by clarifying follow-ups gradually.

AI agent is an intelligent automation component that serves as a bridge between the test management system and the test automation framework ecosystem. This AI agent actively learns, analyzes, and optimizes the testing process. Namely, it can suggest clear, easy-to-understand test descriptions for automated tests — making them accessible even to non-technical stakeholders (Manual QAs, Business Analysts) — and can automatically transform your project into Behavior-Driven Development (BDD) format. Additionally, it detects flaky or failing tests based on execution history.

AI Reporting and Analytics is also our strength. Insights from AI Assistant are not hidden — they are delivered through suggestions in the UI of the test Report. Now the development team has implemented two kinds of AI extensions inside the Report — Project Status Report and Project Run Status Report. These reports are available automatically based on recent test run history. They deliver instant visibility into the health of the test project without delving into individual Test Archive logs.

The AI testing agent provided by testomat.io is an intelligent testing co-pilot. It empowers testers to move faster, test smarter, and reduce risk — all with less manual effort. Below, we break down its capabilities in action and show how its workflow acts.

The Use of AI Agents for Software Testing

AI-driven agents are changing the way software QA engineering teams do their work, enabling them to make the testing process faster, more reliable, and more efficient. Here are the key areas where AI assistants are considered the most reliable helping hand:

Test Case Generation

To speed up the process of creating different test suites, QA and testing teams can use artificial intelligence assistants, which take into account the software requirements and are able to turn simple instructions into test scripts on the fly. Based on Natural Language Processing (NLP) and Generative AI, this process happens much faster and covers a wide range of situations, which would have taken much longer if established with human QAs and testers.

Test Case Prioritization

AI-powered assistants analyze previous test results, code changes, and defect patterns, which help them decide the most effective sequence of test runs. Instead of relying on fixed or random order, these models use data from prior test executions to prioritize tests and optimize the selection of test cases.

Automated Test Execution

AI-based assistants/agents are capable of running tests without QA specialists’ involvement 24/7. When the source code is changed, test suites are automatically triggered to execute continuous testing and provide fast feedback. In addition to that, integrations with test case management systems allow bugs to be reported and all updates to be automatically shared with relevant teams and stakeholders.

Shift-Left Testing

In shift-left testing, AI-based agents deliver faster execution and identify bugs quickly, which enables developers to resolve issues earlier. AI-powered agents can also adapt to evolving project requirements to suggest relevant tests to run based on code changes.

Test Adaptation

Thanks to self-healing capabilities, AI agents can respond to changes in the application’s interface and adjust their actions based on what has been changed.  They can handle UI, API modifications, or backend changes while maintaining automated tests whenever there’s a change in the codebase.

Self-Learning

Thanks to AI agents’ ability to learn from previous findings from tests, they can analyze trends and patterns from past testing cycles, which helps them predict future test results. When learning and adapting, assistants are getting better at identifying potential bugs and making decisions in a jiffy to proactively address them.

Visual Testing

Backed with computer vision, agents can detect UI mismatches across various devices and screen sizes. They verify the aesthetic accuracy of the visible parts that users interact with. AI-based agents aim to find visual ‘bugs’  – misaligned buttons, overlaid visuals (images, texts), partially visible elements, which might be missed during traditional functional testing.

Test Result Analysis

AI agents can review test results on their own in order to find failures and group similar defects. They also point out patterns in the data, which help them detect the root cause faster and focus on what matters most – identifying patterns that may lead to vulnerabilities in the system.

 Overview: Pros and Cons of AI agent for software testing

This table compares the advantages and disadvantages of using an AI agent testing (agentic testing), reminds us about common AI hallucination troubles, and provides a balanced view of AI’s role in testing processes.

Pros of agentic testing	Cons of AI agent testing
Generating test cases that humans might miss. Improve test coverage.	Inability to understand wider context – user intent, business logic, and non-functional requirements that a human tester would comprehend.
Updating the test cases automatically in terms of changes in the code.	Generating test cases, which trigger false positives or false negatives, and requiring careful review before implementation.
Running test suites faster, accelerating the release cycle, and reducing manual effort.	Requiring ongoing maintenance and updates to adapt to evolving testing needs.
Predicting potential bugs based on previous test data.	Having blind spots or ending up with inaccurate predictions when training data is poor and does not cover a broad range of test scenarios and edge cases.
Identifying and fixing broken tests.	Lack of human intuition in complex scenarios.
Self-learning capabilities and adapting testing strategies or techniques based on feedback.	Over-reliance on AI could decrease critical human oversight among test engineers, especially when risks would have appeared in senior and QA manager roles.

How to Test with AI Agents: Basic AI Workflow

When it comes to the entire testing process of the software products, it is essential to mention that AI agentic workflows are Agile process and go beyond simple handling of repetitive tasks.  QA teams should define roles, decide what to test, and what AI agent tool to use.

*It is a plain AI Agent testing Workflow Example; the more sophisticated one was published in a LinkedIn post.  Follow the link to check this AI Agent testing Workflow within testomat.io test management software.

1. Data-Gathering. To get started, AI test agent gather data from many different sources, like APIs, user commands, requirements, past bugs, usage logs, external tools, environmental feedback, and so on, to be trained (if there is a need). Our test management solution supports native integration with many.
2. Collection & Coordination. It is a Role of the Test Management System. Once all the relevant datasets have been collected, the AI-based agents can create relevant test cases to achieve good test coverage, which even covers edge cases, while human testers should approve whether the generated test cases are relevant. Also, AI-powered assistants generate enormous unique test data and user data for email, name, contact number, address, etc., which mirrors the actual real-world data. But when you integrate large language models (LLMs) and generative AI (GenAI), QA agents can rapidly simulate diverse real-world conditions and evaluate applications with greater intelligence and adaptability.
3. Test Execution. AI-powered agents are deployed to autonomously run tests and simulate user interactions to test UI components, assessing functionality, usability, and application performance.
4. Real-time Bug Detection & Reporting. AI-based assistants detect anomalies, frequent error points within the system, and can predict bugs and automatically report defects to stakeholders. In addition to that, it can recognize repetitive flows and high-priority areas for testing.
5. Test Analysis & Continuous Learning. As the software scales, the AI-powered assistants analyse data from user interactions and system updates to keep tests aligned with the application’s current state.
6. Feedback and Improvement. QA team members need to regularly review AI-generated results to maintain software quality. Despite the power of artificial intelligence, it’s important to mention that continuous monitoring and periodic checks of their work guarantee accurate and reliable testing results.

Challenges in AI agent for testing

• When the software product becomes more complex, the amount of computing resources needed for AI testing increases exponentially.
• The absence of representative data leads to testing ineffectiveness – AI-based assistants could develop biases and couldn’t meet ethical standards.
• Using outdated APIs and poor documentation presents a huge challenge for the adoption of AI testing.
• AI-generated test cases and results require careful review before implementation.
• In terms of the AI black box problem, it is difficult for QA teams to understand the logic behind the failure of test cases.

Best Practices AI agent testing implementation

When choosing a test assistant, you need to find the tool that best adapts to your testing needs with the software development lifecycle. Take into account customization, integration, and user-friendliness. However, let us remind!

Do not forget about combining AI and human efforts to balance efficiency and creativity!

Here you can reveal some other tips to help you find the right AI agent testing tool:

1. You need to investigate how your organization is structured, what systems and tools you already use, and what testing tasks you have.
2. You need to define what areas the AI agent testing framework will help you automate before scaling.
3. You need to make sure that your team understands why they need an QA agent in test automation and knows how to use it effectively.
4. You need to discover if an AI bot can be integrated with the platforms you already use.
5. When planning your tool budget, you should consider free, subscription, or enterprise pricing.
6. You need to consider its customisation capabilities to be tailored to your unique testing requirements.

Boost your capabilities with AI Agent Testing right now

Whether you’re a QA lead or a startup founder, applying AI Testing Agents will change the way you carry out testing. They are becoming essential tools for modern QA teams, which can learn from past data and predict failure points. They can also generate different tests, self-heal, and adapt to changes to achieve a higher level of excellence for software delivery in record time.

Are you ready? Contact us to learn more information on how to use the power of Artificial Intelligence agents to create precise test cases and improve the quality and coverage of your software testing efforts.

The post AI Agent Testing: Level Up Your QA Process appeared first on testomat.io.

This list shows tools that work for qa teams with many needs. You will find some testing tools for test case management, and others for continuous testing. These tools help with test creation, test runs, and test coverage. The tools are ready to fix test management problems at any step. This way, your work goes well, and you can give the best results each time.

What are the top AI test management tools currently available?

The top AI test management tools are Testomat.io, Test.ai, Qase, and Zephyr. These platforms use artificial intelligence to help teams with their test management. They make it easy to keep track of test cases. The tools are made to streamline testing processes and improve the way people work together. They also help teams get better coverage for their software projects. All these features work together to boost how much you get done and help keep quality high.

1. Testomat

Testomat.io leads the way with its AI-powered features, intuitive interface, and strong integration with test automation and CI/CD workflows. Built for modern development teams, it helps automate test case creation, self-heal tests when UI changes occur, and provides actionable reporting for decision-making.

QA teams benefit from a clean, collaborative environment where test execution, requirements traceability, and analytics are tightly connected. Testomat.io also includes powerful integrations with tools like GitHub, GitLab, Jira, and Slack, allowing for real-time notifications and fully automated testing pipelines.

This platform supports both manual and automated testing within the same structure, letting teams scale easily without changing their workflows. With its focus on speed, transparency, and AI-powered optimization, Testomat.io gives engineering and QA teams the tools they need to deliver high-quality software—faster

2. TestRail

TestRail is a main place you can use for test case management. It is made for QA teams and helps with both manual testing and test automation. The tool lets you manage testing workflows in one spot, so you get more done.

TestRail gives you one place to keep your test cases. This helps you plan your tests in a good way. You can handle your test runs all in this space. You can link your test cases with what is needed and find defects faster. This makes it easy to follow the work from beginning to end. TestRail offers test automation tools like Selenium and Cypress that keep your testing workflows simple.

This tool is very flexible. You can change the fields and templates to fit your needs. It keeps your data safe because it meets secure standards like SOC 2 Type 2. The qa teams will find the reports clear and easy to read. You can see and know all about the work you do in testing. The tool works well with things like Jira. This will help you move easily through even the toughest projects.

3. Xray

Xray is a Jira tool that helps with test planning. It keeps test cases the same for all. With it, QA teams can work together in Jira in a better way. The tool lets you handle needs and test execution the right way.

There are some key features included. You get exploratory testing as one of them. With agile boards, you can see the progress in real-time. The shift-left setup lets developers and testers work together from the start. Xray also works with test automation frameworks like Selenium and JUnit. This means you can do the same things faster because of test automation.

Charts that show test results make it easy to see your coverage. Managers can know where things are, even for each small part of the work. If you want to move fast and keep things good for your team, Xray is a good match. Both the reports and the way you can use several interfaces fit what teams need when they do agile testing.

4. Zephyr

Zephyr is good for test automation because it is flexible. It helps QA teams if they have hard testing workflows. The tool works for both manual testing and test automation. You get speed and power with it. There is no need to worry about losing either one when you use this tool.

The test case repository helps your group keep track of all assets. It cuts out extra work so things run smoothly. This system works with well-known automation tools like Jira. People can manage their projects together more easily by using it. With the API interface, you get the test data from several channels. You can also connect to other tools with it in a simple way.

With strong analytics features, you can look at test results and spot problems fast. This means you get to fix what slows you down. So, your work gets better with each test you do. If you are sharing work or going over data to fix issues, Zephyr is good for teams of all sizes. It helps your team have smooth workflows and safe connections. This tool supports real success in test automation for qa teams.

5. PractiTest

For qa engineers who work with reporting and keeping track of tests, PractiTest is a good option. The tool gives you the flexibility you need with its features. You get hierarchical filter trees and dashboards. This helps make test case management easy.

You can track requirements and use testing workflows. This helps you stay on top of traceability. You are able to assign tasks and get feedback from people right away. The platform has smart reporting. You can use custom fields and show test info that is simple to read. These tools help teams make their test goals clear.

PractiTest can be used with bug trackers like Jira. This helps the team fix problems faster and easier. You can link your test modules to your main project goals. This makes sure your software stays on track with what you plan to do. PractiTest is made for teams who need to work together. It is good for teams who handle complex QA tasks.

6. Testmo

Testmo is a new test management platform that is made for QA teams. It is here to give you easy and clear answers when you are working with test automation, exploratory sessions, and real-time numbers.

QA teams get a lot out of this tool. They can use clear dashboards that help them watch test runs better. This lets people take out extra work. It keeps testing efforts on track with what the team wants. Testmo also lets qa teams link up with CD pipelines. This way, continuous integration works well and teams get updates that help them.

Testmo gives you features for exploratory testing that are simple to change. You can track where a session goes, but it does not get in the way of how your team likes to work. With powerful reports, your team can see progress, spot trends, and figure out better ways to do well as time goes on. When you use the test coverage tools that come with this platform, it is easier to know what still needs to be tested. This helps your team make smart choices for good QA.

7. QMetry

QMetry helps qa teams do better work in software testing. It uses advanced analytics and machine learning. This gives AI insights that can improve every part of the testing process. The tool helps people in software testing make their testing efforts easier and more clear. You get comprehensive test management because machine learning looks at test results and checks coverage.

When you use predictive analytics for test execution, you can find critical issues before they turn into big problems. This helps you make software development better and keeps you from big setbacks. The user interface is simple and easy to use. You can move through testing workflows step by step without trouble. Small teams and big groups both get to work better, fix defects faster, and keep their testing workflows smooth and well-managed.

8. Kualitee

Kualitee knows that it can be hard to keep test scripts up to date when things in the app change. That is why Kualitee has a self-healing test automation tool. This makes the testing process better for everyone. Kualitee uses machine learning. It looks for changes in the app and updates the test scripts by itself. You do not need to do as much by hand. This lets QA teams focus on other tasks.

This feature helps you keep test coverage strong, so your team can find more bugs. It can also make test execution faster because the tool can keep up with new or changing user interfaces. QA teams will find it easier to work with test case management. They will also get through the software development process faster. In the end, test automation with Kualitee lets teams do more and use their time well during software development.

9. TestMonitor

Real-time risk assessment is very important in software development today. TestMonitor makes a difference by using AI. It looks at test runs to find possible problems so qa teams can make quick and smart choices. With predictive analytics, the tool shows where likely defects can be and how they may affect things. This helps teams use their testing efforts in the right spots. The proactive way TestMonitor does things makes the testing process better. It gives better test case management and smooth workflows. This helps make higher-quality results and a smoother development process.

10. Tuskr

Tuskr uses smart systems to make the defect resolution process faster and easier. It finds issues in the code quickly, so you do not have to spend much time looking for problems. This test management tool uses AI and makes it simple for QA teams to find defects with little effort. With Tuskr, your team can use more time to make the testing process better, instead of going through a lot of manual reports.

The tool uses machine learning that works well with your testing workflows. You get full and complete test management for all your projects. The clear and simple interface of Tuskr helps you track defects. It makes sure that all critical issues are fixed fast. This leads to better software quality, and keeps people happy with what they use.

Key Features to Look for in AI Test Management Tools

When you want to put your money in the best test management solution, you need to know what key features matter for your project. The top things to look at are test automation tools that fix themselves, and that work well with other tools. You should also have real-time analytics that are clear and easy to read. Look for these features if you want a good test management solution.

The platform should help you with test case creation in an easy way. It must use smart tools like machine learning to help make things better. Try to pick a solution that can work with tough test cases but, at the same time, lets you track progress and see your test coverage without much trouble. When all these features are found in one place, you get a good way to meet different needs with less effort.

AI-Based Test Case Generation

Using artificial intelligence for test case generation changes how people do software testing. It lets qa teams work faster and with more accuracy. When machine learning looks at data from old tests, qa teams can make new test cases that cover different situations. This increases test coverage and makes the whole testing process easier. Because AI handles much of the work, teams do not have to do as many steps by hand. The team can then spend more time on important tasks. This helps them get more work done and makes their results better. When you use AI in test case generation, you get smarter ways to test. It fits well with the needs people have in development today.

Integration with Automation Frameworks

Seamless integration with test automation makes the work of qa teams faster and easier. If you use AI tools for test management that fit well with your test automation frameworks, you can improve how you test things and keep high test coverage at the same time. This lets your team manage test execution and test management tasks better. You will also get quick feedback in cd pipelines, so the work moves fast. The development teams and qa engineers get to work together more closely, which helps things go smoothly. Focusing on these integration features makes your automation strong, and it supports fast test case generation. Because of this, continuous testing turns into an easy job that works well throughout the process.

Real-Time Analytics and Reporting

Bringing real-time analytics and reporting into the testing process gives qa teams instant access to useful data. With this, teams get the information they need right away. They can change test plans or manage test case management as needed. This helps improve test coverage and sorts out problems faster. The new and smart charts or images make it easy to read and share test results.

This makes the work in an agile project management team much smoother. When development teams use these reports, they find critical issues much quicker. Over time, this brings better software quality, and the testing workflows get easier so teams can match project needs without trouble.

Self-Healing Test Scripts

Self-healing test scripts bring new ways to do test automation. These scripts can change when the user interface in the application changes. They use machine learning to find out what is new or different in the user interface. Because of this, the test scripts do not need people to always update them. This helps test execution to keep going well without too many stops.

Because of this, QA teams can use their time in a better way. They get to work on the most critical issues. They do not have to spend so many hours on normal updates. This helps boost test coverage. It also gives a good answer for places that change a lot, making the testing process more simple and easy for everyone.

How to Choose the Right AI Test Management Tool for Your Team

Choosing the right AI test management tool starts with looking at what you need for your project. Think about your team size and your test management process. The tool you pick should fit your team’s needs. Make sure this test management tool can work with any test management, automation, and continuous integration tools that you already use. This will help all your tools work well together.

You need to think about how easy the tool is to use. Make sure your QA engineers will not feel it is hard to learn. It is good to look at the pricing models. This way, you are not spending more than you should. Check that the tool has all the features you want for test management. It should fit your budget. When you take time to choose the right tool, your testing efforts become smoother. This will help your team save time.

Assessing Project Requirements and Scale

When you pick an AI test management tool, you have to look at the needs of the project and its size. Each project will have its own goal. It can be manual testing, or sometimes, you may need regression testing for mobile applications.

QA teams need to check the way they work and how they plan things. The team should know about the number of test cases to use, the number of test runs to make, and the tough parts of their software testing. All of this helps you find a good test management tool.

The right test management tool needs to fit all your diverse testing needs. It should make sure you get good test coverage in all of your software testing work. This tool must also work well with changes in your development process.

For example, if you are using agile project management or doing continuous integration, and you add new mobile applications, the tool has to handle that. All these things make your test management ready to grow and change with your team. This will help your testing efforts as your work gets bigger or different.

Evaluating Integration Capabilities

Integration is very important when you choose AI test management tools. It lets different software testing processes work together without trouble. When the tools be able to work with known automation frameworks and cd pipelines, development teams and qa teams get to work together with no big issues. This makes their testing workflows smoother. With good integration, qa teams can make test execution better and get feedback quicker. It also helps qa teams do more software testing in less time.

Being able to connect your tool to the project management system and bug trackers you have now is important. It helps the user experience. This also lets you get more test coverage. When these things work together, you can have higher quality software.

Considering Usability and Learning Curve

Usability is very important when you pick a test management tool. This means a lot for QA teams and for any project members. These people can have different technical skills. If the tool has a user-friendly interface, it makes testing easier and better. It also helps you add the tool to your testing workflows with less trouble. A tool with a low learning curve lets team members get going fast.

They do not need a lot of training to use it. If people want to know more about the software, they can check the tutorials and read the documentation. At the end of the day, you want to find a balance between ease of use and what the tool can do. If you do this, you will streamline testing processes and help your team get the work done well.

Comparing Pricing Models and Support

Looking at pricing models and support options is a big part of picking the right test management tool for software testing. A lot of platforms have their own way to charge you. Some use a monthly subscription. Some tools will ask you to pay one time only, and some let you pay based on how much you use them. Every pricing model comes with its own rules about what features, support, and updates you will get. You need to think about what works with your team, your budget, and your test management plans.

Good and quick customer support is also very helpful. You may need support when you start the tool or if you run into problems later. This type of support can make the user experience better for all people in your company. When you take your time to check these things, the test management tool you pick for software testing will fit your team just right. It will also be able to grow with your development process.

Conclusion

Choosing the right AI test management tool is important for QA teams. A good platform makes test automation and test case management much easier. It also helps team members work well together.

When you look for a test management tool, see how easy it is to use. Check what integrations it offers, and learn about its pricing models. This will help you find a tool that fits your specific requirements. The right choice can make testing jobs simple. It can also help development teams deliver higher quality work for their software.

Always look at what your project needs so that you pick the tool which matches your requirements best.

The post Top AI Test Management Tools appeared first on testomat.io.

With continuous testing, they can make the software testing process automatic and get it done quickly within the DevOps process.

However, they face difficulties when dealing with complicated CI\CD pipelines, strict security requirements, and dynamic cloud infrastructure. Thanks to artificial intelligence, that process is getting less labor-intensive and time-consuming. AI helps create systems that detect problems and heal themselves while optimizing performance and delivering software products which remain reliable, functional, and secure throughout their software development lifecycle.

What is Continuous Testing with AI?

Continuous testing is the practice of testing software continuously throughout the development cycle, typically as part of a continuous integration (CI) or continuous delivery and continuous deployment (CD) pipeline. By integrating testing into every phase of development, teams can catch defects early, improve product quality, and speed up delivery cycles.

When talking about Artificial Intelligence in the context of Continuous Testing, we mean embedding intelligent algorithms which can learn, adapt, and optimize the test cycles. By integrating AI algorithms in continuous testing, artificial intelligence technology helps minimize human involvement in executing tests, improve accuracy, optimize QA activities, and even start a self-healing process.

Generally, ML, NLP, robotics, computer vision, and other technologies come under the umbrella of AI in DevOps. Let’s reveal how they can power the continuous testing process:

• Machine Learning (ML). ML-based algorithms are useful in analyzing historical test data. They applied to identify patterns, make test case selection more effective, predict software defects, and learn from past processes of the test executions.
• Natural Language Processing (NLP). NLP can be used to turn test cases, which have been written in everyday language, into executable scripts and to avoid the need for complex scripting.
• Robotic Process Automation (RPA). With RPA, you can model human actions across various systems and environments to confirm that all the pieces of the app work together correctly.
• Computer Vision. Thanks to AI computer vision, UI elements can be recognized based on their visual characteristics rather than fixed positions, making tests more robust against layout changes and increasing the correctness of UI checks.
• Deep Learning. With deep learning, you can tackle highly specific challenges – identifying sophisticated vulnerabilities in your code repositories, scaling real-time anomaly detection in dynamic systems, and using automated root cause analysis to pinpoint the root cause of complex incidents.
• Predictive Analytics. Thanks to predictive analytics, you can learn information about future events and minimize risks in terms of selecting optimal deployment windows, adjusting resources when scaling.
• Chatbots and Virtual Assistants. These AI-based tools are useful when there is a need to automate interactions with the development team and provide real-time assistance during the development cycle.

We would like to mention that all these types of artificial intelligence require you to learn how to use them for your team’s needs. It’s worth remembering that they are just tools, which will only work if handled the right way in continuous integration automated testing.

Ways To Use AI For Effective Continuous Testing in DevOps

Here are some key ways artificial intelligence is changing continuous testing in DevOps:

Test Case Generation

Teams can apply AI-based algorithms when they need to save time and minimize their effort in creating and maintaining test cases. In this case, artificial intelligence is used to automatically generate test cases in accordance with requirements, user stories, past defect patterns, and code analysis.

Test Execution Optimization

When teams need to optimize the execution flow of tests based on real-time data and new changes in the software application, they can use AI algorithms. They are effective at assessing new code modifications and previous test outcomes to guarantee that the most critical tests are executed first. In addition to that, artificial intelligence can be used to provide parallel execution of tests across multiple environments. In the long run, it enhances test coverage and reduces execution time with faster feedback cycles.

AI-based Defect Prediction

When teams need to predict potential defects and resolve them before they escalate, artificial intelligence algorithms help them do it by analyzing test results and historical data, and identifying correlations between code changes and failures. In addition to that, AI can be applied to monitor application behavior and identify anomalies or unusual patterns in test executions. It can even detect slight visual changes in the user interface that might negatively impact user experience.

Self-Healing Test Suites

Teams have been forced to deal with broken test cases in terms of changes in UI elements, APIs, or system behaviors. Thanks to AI, the process of adapting to changes in the application under test is being done automatically while providing continuous and stable test execution in DevOps environments.

Regression Testing

With AI, the process of identifying and managing relevant regression tests, following unit tests, based on changes and risk assessments, which have been given to each selection, is highly effective and guarantees more comprehensive regression testing. In addition to that, artificial intelligence also investigates areas that need more test cycles, which makes sure they are managed and handled in the right way.

Proactive Continuous Security Testing

Artificial intelligence usage in DevOps can be applied as proactive security measures in order to identify security vulnerabilities through code modifications and code dependencies analysis. It detects security risks and discovers abnormal API call patterns in microservices, which allows teams to move security testing into the initial stages of development and minimize production threats. Also, artificial intelligence helps get real-time visibility and keep an organization’s digital presence under control through continuous attack surface testing.

Why Teams Need AI for Continuous Testing?

Now let’s talk about the main reasons your teams need an AI solution to streamline the testing process.

• Teams can optimize test execution and deal with the highest-priority areas first to prevent things that could really go wrong.
• Teams can win from AI’s capability to predict potential problems, even unusual situations that human quality assurance (QA) might miss.
• With AI, there is no need for the teams to perform time-consuming testing tasks, such as regression testing, exploratory testing, integration testing, performance testing, UI validation, and data entry.
• Based on deep artificial intelligence analysis, teams are in the know the original reasons for bugs, which means they need less time spent debugging and fixing.
• Thanks to artificial intelligence in test data management, teams can deal with faster data provisioning, automated generation, retrieval, and preparation of diverse types of tests instead of doing it manually.
• With artificial intelligence usage, teams can simulate complex and multi-stage attack scenarios and react by integrating continuous penetration testing directly into the CI\CD pipeline.

Key Benefits of AI in CI\CD

These were times when teams handled a lot of tasks manually. With modern technology, however, you can successfully use AI in CI\CD and reap the benefits:

• Pipeline Optimization. With AI, you can understand historical data of test executions and performance patterns. Knowing this information, artificial intelligence can change pipeline settings automatically, which helps find problems and anomalies, suggest fixes, and change resource usage in a jiffy. In the long run, it will lead to quicker build times and more stable deployments.
• Better Monitoring Capabilities. When using AI tools for CI/CD optimization, you can get real-time information about the QA process, alerts, logs, and error detection. Thanks to the ability to consider past logs and errors, artificial intelligence can quickly find the cause of pipeline issues and respond without manual work.
• Efficient resource usage. When applying AI, there is no need to plan resources manually. It automatically scales resources up or down based on what is required.
• Automated Tasks and Improved Security. When it comes to regular pipeline tasks like building, validation, and deploying code, artificial intelligence can automate them and reduce manual testing effort. In addition to that, there is an option of blocking or flagging risky code before it goes into the production environment.
• Code Quality Checks. AI-powered solutions for CI/CD look at code for bugs, style problems, and performance issues. It gives quick feedback to developers, helping them fix mistakes early and keeping the code clean and effective. If there are any issues, they are listed in the logs for manual review.

Challenges in Implementing AI in DevOps Testing

While we can see more benefits of artificial intelligence in DevOps testing, there are also challenges to its adoption. These include:

• You should take into account data quality and its availability. In order to work well, AI requires large volumes of high-quality data, which helps eliminate inaccurate predictions and inefficient QA processes. Furthermore, artificial intelligence should be continuously trained and fine-tuned to improve its accuracy, which requires expertise and resources.
• You may face integration complexity when trying to incorporate AI continuous testing tools with existing DevOps processes. To do this, you need access to quality data from various sources. However, data can be scattered across different departments or systems, making access and integration cumbersome.
• Many existing CI\CD pipelines and test frameworks lack built-in capabilities of artificial intelligence and necessitate additional setup.
• When you decide to integrate AI, you need to remember that it requires investment in infrastructure and whole team training. With increasing AI complexity, you need more computational power to run it.

Best Practices for Implementing Continuous Testing

Before implementing AI in continuous testing, you need to plan strategically and utilize the right continuous testing tools in DevOps. Here are some best practices for artificial intelligence in continuous testing:

• You need to remember that it is not effective to use AI to fix everything and everyone’s issues. It would be a good idea to define a specific testing challenge, which AI can solve. For example, improving test coverage in complex areas or identifying flaky tests.
• To work correctly, AI relies on data. You need to make sure the data is clean and representative to avoid unfair or discriminatory test results. Also, you should choose AI tools and techniques that align with your specific needs and infrastructure.
• You need to decide what you aim to achieve with AI and start doing it step-by-step. You can try the pilot projects without implementing a radical change, and apply AI where it can provide the most immediate value and help you attain established business goals.
• You should combine AI with existing QA methods. There is no need to replace human testers and traditional automation. The task is to automate time-consuming and repetitive tasks which require analyzing large datasets.
• You need to educate your team about how to use AI tools. Also, it is important to maintain documentation where all the information about the AI implementation process, training data, and integration processes is stored.
• You need to train AI models with fresh data to adapt them to your needs when scaling.

Meet more:

• Top 10 DevOps Testing Tools to Improve Your Testing Processes
• Azure DevOps automated Test Management and Testing Automation integration

Let’s sum up

Is Your Infrastructure Ready For AI DevOps Continuous Testing Services?

AI continuous testing is becoming a crucial part of modern CI/CD workflows. When you use AI for continuous integration and testing, this integration promises automation and intelligent decision-making, improves software quality and reliability.

If utilized correctly, AI CI\CD workflow will lead to more efficient, accurate, and reliable software lifecycles. However, you need to remember that AI workloads demand specialized compute resources, flexibility,  and technically prepared team members.

The key lies in a well-prepared continuous testing strategy, continuous learning, and balancing AI-driven test automation frameworks with human oversight and the principles of Continuous Integration (CI) and Continuous Delivery (CD) in mind.

Contact us if you aim to adopt this approach and benefit from faster release processes, improved software quality, and reduced risk of defects.

The post Continuous Testing: AI support in Software Testing appeared first on testomat.io.

What is Risk Based Testing in Software Testing?

As a software testing type, risk-based testing is a process in which the test execution of the QA team should be prioritized based on risk to make sure critical and vulnerable areas receive proper attention.

In the context of Risk-based testing, Paul Gerrard and Nail Thompson determine in the book Risk-based E-business testing:

A risk threatens one or more of a project’s cardinal objectives and has an uncertain probability. Risk is simply a possible mode of failure. When we consider the potential risks of failure in our system, we are making only a speculative bug prediction.

The evidence we gather from risk-based testing reduces some uncertainty, and although we can never eliminate the possibility of failure — which is the case — what testing does is it decreases the uncertainty surrounding a mode of failure.

Thus, by identifying major risk factors, teams identify bugs’ impact and pinpoint which ones will likely cause defects. Let’s consider a real-world use case to know how to formulate a risk-based testing approach

Risk Based Testing Example

Alright, we are moving on! In the banking platform, user authentication and authorization, payment gateway, and credit assessment are high-risk areas because failures can contribute to financial loss, data breaches, and reputational damage.

Conversely, showing an incorrect ATM location or failing to load the map presents a low risk. Just because it may be inconvenient for customers, it doesn’t lead to a security or financial vulnerability.

Discover how teams will act in this situation:

1. They identify functionalities (user authentication and authorization, payment gateway, credit assessment) as high risk due to the severe impact of potential failures while incorrect ATM location and map loading failure are considered low risk.
2. Theyplan and design test cases that will cover every possible scenario as well as assign tasks.
3. They start test executions from high-risk test cases to address issues first, while monitoring and carrying out standard test execution for low-risk test cases.
4. They continuously keep tracking to quickly detect and respond to any new issues.

What is the Purpose of Risk Based Testing?

The aim is to focus QA work on possible areas which are likely to fail or those whose failure would cause the most harm. They are the following:   

• Delivering a stable and reliable product for the most important functionalities.
• To minimize potential negative risks, consequences, prevent financial losses, avoid data breaches,  and protect the company’s reputation.
• To increase confidence in the product’s reliability, performance, and security.

What are Key Risk Categories in Software Testing?

When implementing risk-based testing, organizations typically consider several risk dimensions:

• Business risks: features and functionality which closely related to revenue generation, customer satisfaction, or competitive advantage.
• Operational risks: system reliability and stability, performance under load, or security weaknesses.
• Technical risks: complex code, architectural flaws, new technologies, security vulnerabilities.
• Compliance risks: features which require regulatory or legal adherence.
• Project risks: time constraints, resource limitations, third-party dependencies, insufficient skills

Knowing that, teams should systematically assess each app feature against these types of risks to have a better understanding of where potential issues might arise and what their potential impact is. Here you can find more information about risk management.

Who Performs Risk Based Testing?

When performing risk based testing in software testing, specialists work in collaboration throughout the software development lifecycle. While QA engineers or testers often carry out the execution, multiple specialists take part in identifying, assessing, and prioritizing risks. They are the following:

• QA Engineers(Testers), QA Managers. They are the key specialists who are responsible for identifying, assessing, and prioritizing risks from a testing perspective. They also design, execute, and maintain test cases focused on high-risk areas while reporting bugs and providing feedback on risk mitigation.
• Software Engineers. They fix identified bugs and point out technically weak parts of the system.
• Project Managers. They are responsible for allocating resources among teams or specialists and managing timelines based on risk priorities. They also take place in keeping the team aligned and informed about risks.

Principles of Risk-Based Testing

If you aim to prevent any risks which impact app functionality and launch high-quality applications, you should apply risk-based testing. To use it more effectively, you must understand the key principles of risk based testing. Here are some of those:

• Risk identification. Risks can stem from a variety of sources (technical complexities, integration points, user requirements, and security vulnerabilities) and should be identified. To effectively test, the level of effort should correspond to the software’s risk level.
• Risk assessment. Once risks are identified, they must be assessed to determine their likelihood and impact and focus testing activities where they’ll do the most good, because not every part of the application requires equal attention and needs testing.
• Test prioritization. To prioritize tests for the riskiest areas, either because they’re likely to fail or because failure would cause the most harm.
• Continuous risk monitoring. To make the process ongoing and iterative by identifying risks and splitting them into smaller units for better management.

How Risk-Based Testing Differs from Other Approaches

Understanding how risk-based testing compares to other methodologies is crucial for selecting the most effective strategy for your project.

Why Teams Need to Perform Risk-Based Testing?

• Teams can make certain that potential weak points and critical functionalities receive the most attention during the QA process.
• Teams can prioritize risks in accordance with available resources to make informed decisions about where to allocate their efforts.
• With risk based testing, teams can achieve higher-quality and quicker software releases and minimize the potential for software-related critical issues and the impact of the risk.
• Teams can increase test coverage through risk-based assessment activities such as test case prioritization, resource allocation, and schedule estimation.
• Teams can reduce the manual effort and incorporate test automation to run regression tests on high-risk functionalities regularly.

When to use Risk-based Testing?

Below, you can find information about projects or situations in which implementing risk based testing will be suitable:

• Projects with limitations — time, resources, and cost limitations.
• New projects that require attention in terms of high-risk internal or external factors, like a lack of technological experience or limited domain understanding.
• Projects with a focus on frequent software releases, an incremental and iterative model.
• Projects that lack clear requirements, have poor design, inadequate time planning, or insufficient resources.
• Projects that carry out security testing within cloud environments.
• Projects that focus on security, where risk-based analysis helps identify vulnerabilities.
• Complex projects with multiple integrations or numerous interdependencies which can be challenging to test.

Common Mistakes with Risk-Based Testing

Here is the overview of common mistakes which can delay the Software Testing Lifecycle (STLC) but also impact user experience, business reputation, and customer satisfaction. We will discover which  mistakes should be avoided:

• Teams may delay risk analysis until later phases, rather than beginning it during planning and development.
• Teams may wrongly determine the acceptable level of risk and focus on high-risk areas only.
• Teams may inaccurately identify and resolve risks, which will affect future performance.
• Teams which involved in risk assessment lack the experience or knowledge to fully understand the impact of the test results.
• Teams do not pay much attention when selecting resources to successfully address weak and vulnerable areas.

Benefits Of Risk Based Software Testing

By identifying and analyzing system-related risks, it becomes feasible to enhance the efficiency and effectiveness of test execution.

Let’s reveal the benefits in detail:

Increases testing efforts

With risk based testing in software testing, you can make sure that teams apply resources and their efforts toward the most critical areas of the software. Utilizing this risk based testing in Agile allows them to concentrate their time and energy on the high-priority application areas, which could greatly affect users.

Identifies defects earlier

When teams concentrate on risk based testing, they are able to detect issues and vulnerabilities in the early phases of the project. Such a preventative risk based testing strategy will help them avoid critical difficulties and decrease the likelihood of mistakes which may arise later in the cycle, thereby resulting in higher software quality.

Faster time to market

Using risk based testing often results in quicker cycles. Teams can stop to test everything equally and prioritize the essential features for test execution. When they focus on features with higher risks, they can be quicker validated and verified. Teams can discover critical defects/issues earlier in the development lifecycle and deliver a functional and reliable product to the market faster.

Enhances stakeholder trust

When teams align testing priorities with business risks, stakeholders can trust the QA process more. They understand that testing efforts have a focus on the features and functionalities that are critical to the project success.

Reduces costs

With a risk based testing approach in agile, teams can achieve the most risk coverage with optimal resources for QA work. Thanks to concentrating test efforts on high-risk areas, they can increase bug finding rates in high-priority business features and functionality without growing the test budget, as well as reduce the total cost of the development.

Limitations Of Risk Based Testing

While RBT can help maximize the efficiency of the QA process, it also comes with its own set of challenges. Here are some of them:

Poor planning

Improper planning presents major challenges that may result in a chain reaction of problems across the project’s lifespan. So, there is a need to have a well-structured plan to run critical and priority tests first to prevent the final product from failing.

Potential Overlook of Lower-Risk Areas

When focusing on high-risk areas, lower-risk functionalities might receive less attention. Therefore, it might lead to non-critical bugs being overlooked. In the long run, it can reduce the overall software quality and compromise user experience, even if the high-risk areas function correctly.

Initial Time Investment

While effectively applying a risk based approach in testing, its initial phase needs a significant time investment. So, to set up an RBT, teams should organize a phase for risk identification and prioritization, which involves significant time and effort, especially in projects with tight timelines or where immediate results are required.

Require Skillset Development

The lack of knowledge of risk assessment methodologies can lead to serious consequences in QA work. Teams should be well-versed in how to effectively identify, evaluate, and prioritize risks. Also, they should know about tools or platforms, which are helpful in risk assessment. However, skill development can be seen as a barrier, especially for smaller teams or organizations with limited resources.

Incomplete coverage

When applying a risk-based approach, teams test only critical components of software applications, which means other important components are not yet fully tested. As a result, teams may deal with incomplete test coverage, which may lead to a higher risk of system failures.

Steps in a Risk-Based Testing Approach

The best way to implement a risk based approach testing within software testing is the following:

#1 Step: Risk Identification

At this step, stakeholders, developers, business analysts, and testers come together to discuss areas which are prone to errors. Thanks to their knowledge of the project, historical data and information for similar projects, defect reports, user stories, and industry expertise, they can identify potential issues.

#2 Step: Risk Analysis

Once the risks have been identified, you should undergo analysis to determine their likelihood and potential consequences. A risk-based approach focuses on the highest risks, so you need to define and rank them properly.  While ranking, you need to take into account the complexity of each area and calculate which risks present a significant danger to the organization or only cause minor inconveniences.

#3 Step: Risk Prioritization

Once you have identified and analyzed the risk, you need to prioritize risks. It’s important to mention that not all risks are equally critical. That’s why it is important to address high-impact and high-likelihood risks with the highest priority first, while medium and low priority risks can be solved later.

#4 Step: Test Planning & Test Design

At this step, you need to clearly define the test objectives, scope of testing, test cases, and testing strategy to respond to the identified risks. It’s also imperative to define the approaches, select relevant tools and frameworks, and create tests for high-priority risks. Additionally, you need to allocate the required resources, including test environments, teams, and time, according to the risk prioritization to make certain the test plan is implemented effectively.

#5 Step: Test Execution

Once the test cases are designed and budgets have been approved, you can start the QA process. You need to run tests according to the testing plan. This phase involves conducting tests and actively monitoring and responding to emerging risk information. After this step is completed, the process can begin again as new code, features, and functionality are added to the app.

#6 Step: Risk Control and Monitoring

At this step, you should control and monitor the risk-based test execution. When adding new test cases, updating or removing less relevant tests, you need to take into account new risks and prioritize them. Also, you can add extra test cases and allocate resources for them.

Risk-Based Testing Results Reporting and Metrics

Below you can find some important reporting and analyzing metrics of risk-based testing. Let’s take a closer look below:

Number of Risks Identified

With this metric, testing teams can count the total number of unique risks that have been identified by using various activities – brainstorming sessions, requirements analysis, design reviews, and document them clearly with a test management software for further risk assessment and analysis. Use this metric to evaluate the effectiveness of your risk-identification efforts over time.

Example Calculation of Total Risks

If during a test planning phase, your team identified:

• 5 risks in a brainstorming session
• 3 risks during requirements review
• 2 risks from defect history
• And 1 duplicate across these was later merged

Total unique risks = 5 + 3 + 2 − 1 duplicate = 9 risks identified

 Notes: Count unique risks, not duplicates.

Risk Priority Number

This metric can be used for quantitative or qualitative assessment and to determine which areas of the software or which functionalities should receive the most attention and effort. It helps test teams to rank identified risks based on their consequence and probability of risk. Risk Priority is usually calculated using a formula based on the Risk Priority Number (RPN), which follows the common method of risk-based testing and Failure Mode and Effects Analysis (FMEA).

In this formula:

• Severity – mark of how serious the impact would be if the risk occurred (e.g., scale 1–10)
• Likelihood – likelihood the risk is to occur (e.g., scale 1–10)
• Detectability – estimation of the issue detection before reaching the user (e.g., scale 1–10, where 1 = easily detected, 10 = hard to detect)

Example Calculation of Risk Priority

Suppose for a specific risk:

• Severity = 8 (high impact)
• Likelihood = 6 (moderately likely)
• Detectability = 7 (not easily detected)

Risk priority = 8 × 6 × 7 = 336

Notes: The higher the risk’s priority, it should be treated as a high-priority risk.

Number of test cases planned VS executed

When applying this metric, teams can measure the proportion of prepared test cases compared to the total tests which have been planned. Knowing that information, they can understand whether the test preparation stage is going according to plan and there are no delays or issues in test case development process. This metric helps QA teams track progress during test cycles, identify bottlenecks in test execution and report status to stakeholders. Also, tell about the current state of quality and test activities.

Example of execution rate

• Planned test cases: 120
• Executed test cases: 90

Execution Rate = (90 / 120) × 100 = 75%

Test cases executed

With this metric, teams can overview of how many tests are succeeding versus how many are failing. When teams see a high number of failures, it indicates instability or bugs in features which are not yet complete or working correctly. A high failure rate means the QA work is effective in finding issues.

Example metric of Executed Test Cases

• Passed 80
• Failed 10
• Skipped 5
• Blocked 3
• Not Run 2

Test Cases Executed = 80 + 10 + 5 + 3 = 98

Notes: Test Cases Executed = Total Test Cases with Status ≠ Not Run or Blocked

Defect density

This metric shows teams how many bugs are in a specific piece of code (defect density) and how critical that piece of code is to the business or system (risk identified). It helps spend less time on low-risk and low-defect-density areas and use the budget and schedule efficiently. Most teams calculate defect density as the number of defects per thousand lines of code (KLOC), but some of them count per module or function points.

Example Defect Density Metric

• 20 defects in a module
• 10,000 lines of code

Defect Density = (20 x1000) / 10,000 = 0.2 defects per KLOC
For example, if you find 2o bugs in 10 modules, the bug density will be 20 ÷ 10 = 2 per module

Number of test cases of high severity still open

Thanks to this metric, teams understand that there are still issues that block the software from being released and could have severe consequences – system crashes, data loss, major functionality breakdown, security vulnerabilities without fixing. It helps measure unresolved risk before release, drives prioritization during regression or release readiness, Informs stakeholders about potential blockers.

Example of Number of test cases of high severity still open

You have 50 test cases marked as high severity:

• 20 passed
• 10 failed
• 5 blocked
• 5 skipped

Remaining high-severity test cases still open: 50 – (20+10+5+5) = 10

Test effectiveness (risks identified and mitigated)

With this metric, teams can validate whether risk assessment and identification are accurate. They can also understand if a risk-based approach works or if their strategy and coverage need to be re-evaluated. Guides continuous improvement for test planning and execution. Helps stakeholders assess test quality and coverage.

Example of Test Effectiveness

• Total risks identified: 20
• Risks successfully mitigated during testing: 15

Test Effectiveness = (15 / 20) × 100 = 75%

75% test effectiveness means a strong alignment between risk identification and resolution.

Test coverage report

This metric determines the level to which your efforts have covered the codebase. Teams can assess the completeness of their effort and be in the know how thoroughly the software has been validated.

Example Automation Code Coverage Report

• Total test cases: 1,200
• Automated Test cases: 960

Automation Code Coverage = (960 / 1,200) × 100 = 80%, Risk coverage by report

Risk coverage

When considering the risk coverage, teams can find out how much of the company’s risk is covered by test cases. They will have a clear understanding of how effectively the test strategy has been implemented and see which identified risks have been addressed or covered by test activities.

Example Risk Coverage report:

• Total Identified Risks: 20
• Risks Covered by Test Cases: 15

Risk Coverage = (15 / 20) × 100 = 75%

Applying this metric allows testers to see if they tested the right issues/bugs effectively and focus on the most important risks. They can also find out which identified risks are now fixed or handled and why their efforts were spent on specific crucial areas. They can also discover if any big risks remain and if the software is ready to launch.

When tracking metrics mentioned above, you can make certain that your efforts are thorough and consistent. While there are a lot of other metrics you can track, it is essential to use those that can get you closer to your business goals and launch high-quality software.

Comprehensive Summary Risk-Based Reports

Risk identification testing based matrix give us clear visibility, making it easier to understand the current level of risk exposure. Support more efficient use of testing resources by reducing time spent on low-risk areas. Vice versa, pay attention to higher severity failure areas which have a higher impact on risks. At the end of the day, it gives us a clearer objective to aim for from a testing perspective. We’re not just guessing what’s important; we know what’s important now.

Use risk based traceability matrix to ensure each risk has one or more test cases linked. If we diagnose the severity of risk as high, usually we retest such test cases using regression testing along with risk based testing to reduce the perception of failure in the future.

Summary risk based testing reports help QA managers provide information clearly to business stakeholders, as well. They can reassess risks with greater confidence, knowledge, and information to decide to release or not release or continue testing.

Best Practices For Implementing Risk-Based Testing

Below we highlight some tips and tactics to follow to lead to a successful RBT implementation:

1. Before evaluating risks, you need to define clear and consistent criteria for them. Also, you can use risk prioritization matrix and engage all stakeholders into the risk prioritization process.
2. You can apply a risk breakdown structure to help all engaged parties identify the risk prone areas and categorize many sources from which the project risks may arise.
3. You can use risk-based metrics for tests, which can help you visualize and show a lot of relevant information about test efforts, like the risk closure and status.
4. You can utilize test automation frameworks and AI-based tools to speed up the RBT process and improve quality.
5. You can use test cases that cover essential functionalities, consistently update and maintain in order to keep them relevant.
6. You can apply a test case management tool like testomat.io to streamline the implementation of RBT in agile for better risk identification.

Bottom Line: What About Using RBT?

Risk-based testing approach allows teams to prioritize the critical functionality of the software or system. When using this approach, you can optimize the QA process and target your testing efforts towards the areas of your application that are most likely to cause problems. By identifying, assessing, analyzing, and mitigating risks based on their prioritization, they can eliminate over-testing and focus on checking the most critical areas.

However, before establishing risk based testing approach, you need to make sure that communication between the stakeholders, software engineers and testers, which are engaged in the software project, is open and clear. They should do their best to identify and address potentially critical risks in the developed software products.

Drop us a line if you are interested in adopting RBT (Risk Based Testing) in your QA process.

The post Risk-Based Testing: Strategy, Approach & Real-World Examples appeared first on testomat.io.

This breakthrough became possible thanks to the key role Agile testing plays in ensuring accuracy and rapid adaptation to changes. That is why it has become an integral part of the modern development process. To move confidently in the new programming environment, it is important to understand the impact and value of this approach.

What is Agile Testing?

Agile testing is a software testing method based on the principles of Agile development. The testing process involves not only Agile testers but also all team members, including analysts, developers, and other specialists within their areas of responsibility. Unlike the classical approach, where testing is a separate phase, here it is integrated at all stages – from requirements gathering to design, writing new code, and creating test scenarios.

This approach is characterized by flexibility and continuity: testing is conducted throughout every cycle of the software product development. Its main goal is to meet the customer’s needs, which is achieved through constant verification of the product’s compliance with end-user expectations.

Agile testing is usually informal, conducted regularly within each iteration of the software development life cycle, and aimed at maximally considering user requirements at every stage.

The concept of Agile Testing Methodology

Agile testing methodology refers to the approach and set of practices applied to test software in an Agile development environment. In this approach, testing is not separated as a distinct phase but is organically integrated into all stages of the software product life cycle.

Its principles are outlined in the Agile Manifesto. It focuses on collaboration, adaptability to change, and continuous feedback.

Agile testing, in its essence, is a testing process conducted in the context of the Agile environment. While many types of testing — such as regression or functional testing — are also common in other development approaches, in Agile teams, the very methodology of their execution changes. This includes how testing is planned, integrated into the release cycle, and how its effectiveness is assessed.

Traditional Testing Method VS Agile Software Testing

To avoid situations where software breaks during real user operation, testers are the first to try to identify all its weak spots — and pass them on for improvement.

In the classical waterfall model of software development, the process looks like a sequential chain:

Requirements Gathering → System Design → Implementation → Integration and Testing → Deployment → Maintenance.

In this approach, each subsequent stage begins only after the previous one is completed. Because of this, the QA team receives the product for testing only at the final stages of development. Errors detected at this stage are much harder and more expensive to fix than during earlier phases.

When testers are involved only at the end of development, they do not have the opportunity to ask important questions or conduct full-scale testing, as they receive almost no feedback from either the clients or the rest of the team. Sometimes, they are even perceived as an external element. In such cases, testers simply wait until the product reaches them through the production line, and only then do they test it — having limited tools to determine whether it should be sent back for revision.

In Agile development, the approach is completely different: the test plan accompanies the entire product creation cycle. Each time the code is updated, the test team immediately gains access to it, checks it, and provides feedback directly to the developers. These short testing cycles may also include automated testing and the participation of real users, which allows for the quick detection and correction of flaws. Let’s total briefly with a table, compare the Waterfall Model and Agile once again:

	Waterfall	Agile
Requirements	Clearly-defined	Can change
Client involvement
Type	Straightforward	Complex projects
Time frames	Strict	Loose
Team collaboration	Moderate	Intensive

Implementing Agile testing methodology requires focus and responsibility; however, the results can significantly improve product quality. At the same time, a careless application of the Agile approach without evaluating its appropriateness can do more harm than good — leading to an unstable and unreliable system.

Here are situations when it is worth considering whether Agile testing is truly appropriate:

Clearly defined project scope

If the project requirements are final and not subject to change, the dynamic nature of Agile may not provide additional value.

A single influential customer

In projects where one person or a small group makes all decisions, the collaborative model of Agile may conflict with centralized decision-making according to customer requirements.

Insufficient level of competence

Since Agile testing demands broad knowledge and the ability to work at a fast pace, teams without adequate preparation may find traditional approaches more comfortable.

Customer insists on waterfall

If the client expects a traditional development and sequential staged approach (waterfall model), Agile may cause resistance and misunderstanding.

Agile Testing Principles

Twelve principles were formulated after the creation of the Agile Manifesto. They serve as a practical guide for teams that aim to work in accordance with the Agile philosophy. These principles help evaluate how well the team’s current actions align with the spirit of Agile and demonstrate how this approach should be implemented in everyday work.

Here is a brief summary of the key principles:

1. Early and frequent testing. The earlier errors are detected, the easier they are to fix. That is why testing should start as early as possible—even during the requirements gathering phase.
2. Test automation. Creating automated scripts allows for faster verification of changes. However, it is important not to overestimate the capabilities of automation and not to expect a higher level of automation than is realistically achievable.
3. Continuous feedback. A tester’s task is not only to find bugs but also to ensure fast communication of test results between the client and developer to quickly improve the product.
4. User value comes first. Although this is not the first item on the list, focusing on delivering value to the client is the main goal of the entire development and testing process.
5. Live communication. Testers should interact directly with developers and users — this reduces the risk of errors and misunderstandings.
6. Simplicity is efficiency. A tester should perform only necessary checks, but not overlook any critical ones. In product work, simple solutions are preferred if they provide maximum benefit.
7. Continuous improvement. Agile professionals constantly seek new ways to make their work better and more productive.
8. Adaptability to change. Agile teams quickly adapt to new conditions — whether it’s changing requirements, user feedback, or market dynamics.
9. Self-organization. Testers do not wait for instructions—they independently identify where their involvement is needed and unite the team around solving the problem.
10. Focus on the end user. People are the priority. Communication, convenience, and practicality are critical.
11. Daily collaboration with the business. Developers and business representatives should work side by side every day to better understand customer needs and quickly implement changes in the product.
12. Regular reassessment of work. The team should periodically reflect on their approach, seek improvements, and adjust behavior to achieve greater effectiveness.

Agile Runs on Continuous Feedback

Customer Feedback and Close Collaboration with Them – Above All!

The agile approach to testing values customer needs above the company’s internal requirements.

Testing throughout the entire development process results in a higher-quality product and ultimately greater customer satisfaction.

Instead of the client receiving a pig in a poke at the end, where problems may arise that are difficult (or even impossible) to fix, the agile approach allows the detection of failures and issues as they occur.

The customer plays a much more active role throughout the entire development process. The role of collaboration with clients in agile testing is much more significant than just negotiating contract terms. They can provide their suggestions and feedback at every stage. This allows tailoring the product to the customer’s needs and expectations, which leads to a much better outcome.

Agile methods may either include the end user as a full member of the team (who attends all meetings and monitors goal achievement) or involve them periodically for product demonstrations. Although some clients may not care how exactly your product is created, absolutely everyone expects a result that satisfies them.

Thus, the agile process provides transparency that no other approach can ensure since clients are involved in the development process from start to finish.

Agile Testing Life Cycle

The Agile Testing Life Cycle (ATLC) is the process QA teams follow to plan, implement, and execute testing in Agile software development environments. Agile testing is continuous and iterative, happening in each sprint.

The Agile testing life cycle consists of five main stages:

1. Impact analysis, Requirements Gathering. This initial stage is also known as the feedback phase. Opinions and feedback from users and stakeholders are collected. This allows testers to clearly define the goals for the next testing phase.
2. Agile test planning, test design. At this stage, the testing efforts of developers and testers are combined with the input from customers and stakeholders to create a detailed plan: the testing schedule is discussed, the frequency of meetings is determined, and the expected results are defined.
3. Release readiness check. At this stage, testers analyze the implemented features to determine if they are ready for launch. Functionality that does not meet quality criteria is sent back for revision to the previous development phase.
4. Test Execution. As development proceeds, tests are run continuously — both manually and automated tests through CI pipelines. Automated regression, functional, and unit tests are executed with each code change.
5. Daily stand-ups (Daily Scrums). Every morning, the team holds brief meetings to align on the current testing status and set goals for the day. This helps to respond quickly to changes and improves coordination.
6. Agile test evaluation. The final stage includes weekly meetings with stakeholders, during which progress is analyzed and actual results are compared to the planned objectives.

Agile Testing Strategies tailored for AI-assisted testing

If you are confident that Agile testing aligns with your development team’s goals, client expectations, and product characteristics, follow these recommendations:

Choose the appropriate methodology

Determine which of the Agile approaches (e.g., Scrum, Kanban, or Extreme Programming) best suits the needs of your project.

Build an effective testing strategy

Focus on the four testing quadrants to cover all key aspects of product verification. We are talking about it ahead.

Define objectives with AI in mind

Identify areas where AI can provide the most value — like generating test cases automatically or test data, optimizing regression tests, maintaining tests, detecting flakiness or blind spots in your testing project. Select AI-powered test platforms or AI testing agents.

Ensure team collaboration and avoid tester isolation

• QA team should work closely with developers. Constant communication facilitates quick responses to changes and helps form a unified vision.
• Adhere to iteration-based work. Collaboration at each stage helps establish effective interaction from the very beginning of the project.
• Define a human-AI collaboration approach. Clarify how human testers work alongside AI — reviewing AI-generated tests, approving AI suggestions, or AI-generated insights of release readiness.
• Provide feedback. Tester comments and suggestions are important for both developers and customers. This enables two-way communication and improves the final product.

Here is a structured breakdown of the key steps within a testing strategy, which can be applied to implement Agile successfully:

#1: Iteration 0 

This is the very first step in the testing process, during which the foundation is laid for all further work. At this stage, the testing environment is configured. This phase includes performing preparatory tasks:

• searching for test participants
• preparing the necessary materials and resources for usability testing
• justifying the project from a business perspective
• defining boundary conditions and the overall scope of work
• formulating key requirements and usage scenarios
• outlining one or more possible project architecture options
• outlining how and when tests will be executed—manual, automated, scheduled in pipelines
• planning the initial project budget and estimating costs
• choosing testing tools
• identifying potential risks.

#2: Construction Iteration 

This is the second phase of the testing process, which is the main and most labour-intensive. It consists of a series of repeated cycles (iterations) aimed at gradually building and refining the software product. This process is divided into two types of testing:

First, Confirmatory Testing — verifies that the system meets stakeholder requirements as communicated to the team at that time. It is performed by the development team itself and is further divided into two subtypes:

• A. Agile Acceptance Testing. This is a combination of acceptance and functional testing. It can be conducted either by the development team or the stakeholders. On this stage, testers often work following test design techniques like boundary value analysis, equivalence partitioning, state transition, or BDD scenarios.
• B. Developer Testing. This is a combination of unit and integration testing that checks both the software code and the database structure.

Second, Exploratory Testing identifies problems that were missed or overlooked during confirmatory testing. In this type of testing, the tester identifies potential problems in the form of defect stories. It focuses on areas such as integration testing, load testing, security testing, and stress testing.

#3: Analysing Testing Efforts

Define defect tracking, reporting, and analytics tools, including AI-powered anomaly detection tools; how issues will be triaged, prioritized and notified to the team.

#4: Release End Game

This phase is also known as the transition phase. It includes full system testing and acceptance testing. To complete the testing stage, the product undergoes even more thorough checks than during the development iterations. Testers work on fixing identified defects. This phase includes the following activities:

1. Training end users.
2. Preparing support and operations staff.
3. Marketing activities related to the product release.
4. Backup and data recovery procedures.
5. Finalization of system and user documentation.

#5: Production

This is the final phase of Agile testing. At this stage, the product is finalized after all identified defects and issues have been resolved.

Agile Testing Quadrants

Agile testing is divided into four quadrants, each covering a separate area of the software verification process.

Quadrant 1 (Automated)

This stage focuses on the internal quality of the software code. Technically-oriented test scenarios are created and executed here, primarily automatically by QA testers. The main goal is to ensure the technical stability of the code.

Types of testing performed at this stage include:

• Unit Testing – checking individual functions or code fragments for correct operation.
• Component Testing – analyzing larger blocks of functionality that combine multiple parts of code working together.

Quadrant 2 (Manual and Automated)

At this stage, the emphasis is on business requirements, which testers receive from the customer both before and during development. User-oriented scenarios are created and executed either manually or through automation.

This quadrant includes the following types of testing:

• Pair testing – two specialists test the software simultaneously, exchanging ideas for faster error detection.
• Scenario and workflow testing – verifying whether the program’s behavior aligns with business requirements.
• Verification of user stories and prototypes – assessing whether they meet the expectations of future users.

Quadrant 3 (Manual)

The third stage provides feedback for the first two. Testing is conducted in several iterations, after which the team improves the code. Although this stage is often associated with preparing for automation, the process itself is mostly manual.

This quadrant includes:

• Usability Testing – evaluating the intuitiveness and ease of use of the interface.
• Collaborative Testing – close interaction between testers, the development team, and stakeholders.
• User Acceptance Testing (UAT) – verifying whether the product meets the expectations and needs of end users.
• Pair Testing with Customers – working side by side with clients to quickly gather feedback.

Quadrant 4 (Tools)

The final stage focuses on verifying the non-functional characteristics of the product, particularly its reliability, security, and performance. Specialized tools are used here for in-depth analysis.

This quadrant includes:

• Security analysis
• Scalability testing
• Infrastructure verification
• Data migration assessment
• Non-functional testing – including load, stress, and performance testing.

These four quadrants allow for coverage of both functional and non-functional aspects of development, ensuring comprehensive testing in an Agile environment.

5 Key Types of Testing in Agile

Several approaches have been developed for implementing Agile testing, each offering its own advantages depending on the context of the project. Below are four of the most popular methodologies actively used today. Although none of these methods are universally applicable, they serve as an effective foundation for building a custom-tailored testing strategy.

Test-Driven Development

In this approach, test cases are created before writing the main code. The methodology includes three sequential stages:

• defining what exactly needs to be tested
• forming a user story
• creating a unit test
• writing the code that allows the test to pass;
• refactoring

This process ensures that every part of the code is tested and improved gradually in clearly controlled steps.

TDD is typically applied to unit and component testing, which is often automated using specialized tools. This approach helps ensure that the functionality is implemented correctly and meets the requirements.

Acceptance Test-Driven Development

ATDD is a variation of the TDD (test-driven development) approach, which enhances team collaboration in an Agile environment. Here, clients, developers, and testers jointly define acceptance tests, focusing on customer needs. Only after these tests are created does the team proceed to develop functionality, which ensures a clear understanding of requirements and minimizes the risk of errors. Afterward, functional tests are conducted.

Acceptance testing highlights:

• requirements are very clearly analyzed without any ambiguity
• acceptance tests serve as a guide for the entire development process
• encourages collaboration among cross-functional team

In addition, this approach simplifies the creation of test scenarios and allows developers to better understand user expectations, reducing misunderstandings in the process.

Behavior-Driven Development

This is a logical continuation of the TDD and ATDD approaches, focusing on achieving expected business outcomes. At the core of this method is describing system behavior in language that is understandable to all project participants. Testing is performed according to described user behavior scenarios.

The BDD process includes five main steps:

1. Formulating expected behavior.
2. Writing the scenario steps.
3. Running the test with the expected failure.
4. Implementing the functionality to pass the test.
5. Retesting until success is achieved.

Uncover specifics

• The Ultimate Guide to Acceptance Testing
• ATDD vs TDD: Understanding the Key Differences
• Behavior-Driven Development (BDD): Meaning, Benefits & Best Practices
• Exploring TDD vs BDD: Which is Right for You?

Exploratory Testing in Agile

This approach involves continuous learning during testing: the tester simultaneously creates and executes tests, analyzes the results, and plans further actions based on them. Exploratory testing is not based on predefined scripts — instead, it allows for a creative approach to discovering bugs that traditional testing might miss. This method makes it possible to flexibly assess the product’s working condition and how it will behave in real use.

Session-Based Testing

This approach extends and structures exploratory testing by providing it with clear boundaries. To avoid chaos, each testing session begins with defining a clear objective (a charter), after which the tester focuses solely on its completion for a limited time.

The results of each session are documented, and the tester reports to the lead afterward. Such structure ensures that no part of the product is overlooked and allows for effective progress management in testing.

Agile Testing Supports Continuous Testing

Agile testing is based on the idea that continuous verification is an integral part of development and is as important as writing code. Within Agile methodologies, testing is tightly interwoven with the development process, allowing bugs to be identified at early stages and on a regular basis. This approach enables testers to detect issues throughout all stages of product development, significantly speeding up its release readiness.

By combining continuous integration and continuous delivery with flexible testing approaches, development teams can ensure that their software product is always ready for release and can easily adapt to any changes or new market demands.

Agile-focused testing practices – building resilience and optimizing team output.

Thus, the continuous testing inherent to the agile method allows creating a product that is both high-quality and fully meets the customer’s expectations.

Advantages of Agile Testing

Implementing Agile testing brings three simple yet significant benefits: a satisfied team, a higher-quality product, and faster time-to-market. This Ideal Trio is a strong argument for building an effective Agile testing strategy.

1⃣ Higher-Quality Product

Thanks to continuous feedback, Agile allows errors to be detected early in development. Testing is launched simultaneously with code writing, so bugs are eliminated immediately rather than accumulating until the final stage. Each product iteration undergoes a complete cycle of testing and improvement. Continuous verification and close collaboration among stakeholders facilitate a better understanding and the development of a truly high-quality solution.

2⃣ Fast Delivery

Unlike the waterfall model, where months or even years can pass between development and launch, Agile allows this gap to be significantly reduced. The product is gradually improved during the development process, guided by regular user feedback. This helps to quickly adapt to market needs and deliver the solution to end users in the shortest possible time.

3⃣ Happier Team

The final principle of Agile testing is job satisfaction. Constant interaction among team members creates an atmosphere of collaboration, trust, and mutual support. This approach not only boosts productivity but also makes the workflow more comfortable and inspiring.

As Lisa Crispin and Janet Gregory noted:

“A team that guides itself with agile values and principles will have higher team morale and better velocity than a poorly functioning team of talented individuals.”

Transition to Agile Testing

Remember, every company is its own separate world, so the path to agile thinking will be unique for each. Therefore, don’t be afraid to improvise and adapt everything to suit yourself!

Communication within the team and working side by side – that’s everything for us!

In Agile philosophy, the main thing is people. It is they, living people, who respond to business needs and essentially steer the development process. Therefore, if we want to cultivate agile thinking for testing, it is extremely important that everyone’s voice is heard.

Agile is all about ensuring the client is satisfied by receiving software that works without long pauses.

And for that, testers need to learn not to stay silent but actively communicate. Because the goal is the same for everyone – to create a truly great product and provide users with best practices.

Forget about those times when departments sat in their own “cubicles” and everyone was on their own. If we choose the Agile path, communication is alpha and omega. Agile requires the team to work as a single organism, not that each tester is by themselves or each department works alone.

Quality is the shared responsibility of the entire team, not just the tester.

When everyone is together as one team, this not only helps to achieve the main goal – to deliver a valuable product to the user – but also protects against delays in work.

Moreover, such an approach allows squeezing the maximum from the process: to find product defects faster and, most importantly, to work proactively, preventing problems rather than just fixing them afterward.

What Agile Teams Need to Run Like Clockwork?

• Have clear common goals and intelligently distribute work among teams (if there are several). This provides autonomy and ensures that no one duplicates the same work, creating unnecessary dependencies.
• Remove walls between departments. To succeed in the agile transition, it may be necessary to reconsider the usual communication culture and even the company structure to support teamwork.

If you manage to successfully transition to agile rails, there will be many advantages. And by developing an agile mindset and implementing agile processes, these advantages can be fully realized.

What might come in handy to establish an Agile testing process?

→ Tools for collaboration and integration.
→ Visual boards (so everyone can see what’s going on).
→ Short Daily Stand-ups between teams.
→ Joint release planning for all teams.

Learn to Change and Develop Agile Quickly

Training

Perhaps the best way to smoothly transition to agile rails is to undergo proper training. If you want yourself and your team to develop the right agile mindset for testing, good training will help a lot here.

Unlike what testers of the “old school” might be used to, agile testing is not about final checks before release or just bug hunting. It happens continuously, is woven into the development process itself, and does not wait for the finished product.

Why so? Because agile testing is primarily a tool for preventing problems, not just detecting them. It helps improve individual parts of the product and the testing approaches themselves. Therefore, training will be useful for everyone who wants to move to Agile.

Fast feedback

The agile process also relies on fast feedback, which we have known already. Since the product is tested continuously, testers need to learn to respond instantly and adapt – this is the key to a successful transition.

Continuous testing requires changes: in people, in processes, in technologies. Therefore, cultivating an agile mindset to implement these changes is extremely important.

As part of development, automated tests that provide quick feedback considering risks are critically important in Agile. When testing is integrated into the development process, the team can be more confident that the user experience is reliably protected.

Focus On a Working Product, Not Mountains of Documentation

Usually, when software products are created, there is an attempt to document them thoroughly for development and delivery.

Creating all this paperwork can take a lot of time because the list of necessary documents is quite large: technical specifications and requirements, interface design documents, documentation and testing plans.

The time and attention consumed by this documentation often cause delays in development. Although documentation is still needed in Agile, the approach to it is simplified to avoid such slowdowns.

Besides, the tester can focus more on the software itself and on those actions that truly add value and, as a result, make clients happy.

Smart documentation plays an important role in agile testing, allowing the focus to remain on the end product.

In agile projects, the test plan is usually very concise, so it is recommended to supplement it with a strategy describing exactly how the system should be tested. This follows from how agile projects function, especially the fact that testers work in short cycles (iterations).

Documents commonly used in Agile testing may include:

• Test strategy (how we will test the system)
• Test plan (concise)
• Checklists for setup and regression testing
• Test specifications.
• Ideas for exploratory testing
• Test logs and lists of found issues

Responsibilities of an Agile Tester

An agile environment requires that a typical Agile testing professional has to meet the following traits:

Conclusion

The Agile methodology has gained wide popularity as an effective approach to project management not only in software development but also in many other fields. Its natural flexibility and ability to respond promptly to changes make this model one of the most relevant in today’s world.

With proper implementation and sincere commitment of the team to its principles, Agile can provide numerous benefits and allows teams to remain flexible, resilient, and productive even under challenging conditions.

The post Agile Testing: Methodologies, Real-World Practices & Performance Insights appeared first on testomat.io.

Software testing plays a key role in development. It needs careful planning and effort. You also have to keep track of everything. This is where test management tools become useful.

They make software testing simpler. They help teams manage test cases, automate testing efforts, and understand test results better. In this blog post, we will explore the best test management software and test case management tools that QA teams will need in 2025.

Top Test Management Tools for 2025: A Comprehensive Review

The year 2025 will bring major changes in test management tools market. As software development methods evolve, we need testing tools that are better and easier to use. These tools should meet various testing needs. They must work well with existing systems and provide clear information. This will help produce high-quality software.

This review highlights the best test management tools that might lead in 2025. Each tool has unique strengths and suits different needs and team sizes.

If you want easy integration with your current tools, advanced automation options, or a simple interface with clear reports, this review will help you find a solution that meets your testing goals.

1. Testomat

Testomat is a top company in test management. It makes handling testing efforts simple and accurate. Its advanced features and easy (better highlight reusable design capabilities, flexible workflow) design support test planning and execution.

Testomat works well with many tools to ensure full test coverage and keeps track of test results. This platform helps development teams work together and makes testing easier. Testomat focuses on quality assurance and innovation. That’s why it is a popular choice for test management.

Key Features of Testomat

Let’s explore the key features that make Testomat an excellent choice for test management. Testomat offers helpful tools and an easy-to-use design. This allows QA teams to enhance their testing processes.

Testomat is a great test case management system. This platform lets users create, organize, and run test cases easily. It makes testing thorough and dependable. Teams can combine test cases into test suites. This way, they can stay organized in their testing efforts.

Testomat has a very important feature. It can easily connect with popular development tools. If your team uses tools like Jira, Slack, or any others, Testomat will work well with them.

• Creating and organizing test cases is simple.
• It has smart features for test automation.
• Reporting and analysis are clear.
• It integrates easily with development tools.

Testomat Pricing and Plans

Testomat knows that every team has unique needs and budgets. That’s why they provide different pricing plans to fit many situations. This means anyone, from a small startup to a large company, can find what they want. Whether you are new to test management or want advanced features and support, Testomat has the right subscription plan for your team.

Testomat’s pricing is simple and transparent. You can view the features and limits of every plan on their website. This helps you choose the plan that works best for you. They also offer a generous free trial. This shows that Testomat wants to give good service and make it easy to access.

User Experience and Customer Feedback

Testomat really cares about user experience. They show this by listening to the important feedback from customers. Users often talk about how good the platform is for its simple interface. Many like how easy it is to use. This makes it popular among both experienced testers and those new to test management.

Customers mention that the platform is quick and reliable. A straightforward switch from manual to automated testing helps with test execution. Receiving fast feedback during testing makes the process easier. Additionally, the links to popular tools for communication and teamwork boost collaboration and ensure clear communication.

Testomat wants to make sure its users are happy. Their support team responds fast and tries to get better by hearing what users have to say. They often add new updates and features based on customer suggestions. This shows they want to offer a tool that meets the changing needs of QA teams.

2. TestRail

TestRail by Gurock is a good choice for easy test management. It works well with many popular tools. This helps you track test execution and create detailed reports.

TestRail makes it easier to manage test cases and control versions. This improves overall quality assurance. Its simple design allows teams to work better together, which can increase productivity.

With advanced features like custom fields and test case templates, TestRail simplifies the testing process. It is an important tool for development teams that need better test management.

Highlighting TestRail’s Core Features

Let’s look at the key features that make TestRail one of the best test management solutions. These features help QA teams improve their work. They make both test planning and test execution easier.

One important part of TestRail is its effective test case management system. Users can easily create and organize test cases. They can save these cases and customize fields and templates to include important details. The platform is user-friendly, allowing teams to quickly find the test cases they need.

TestRail makes test planning easy. It allows you to create and manage test plans and test runs all in one spot. It connects well with popular issue trackers. This feature helps teams link test cases to bugs or issues. It also makes tracking simpler during the testing process. You can view real-time reports and helpful dashboards that display your testing progress. This way, teams can make decisions based on accurate data.

• A single place for test cases
• A flexible way to plan and schedule tests
• Detailed tracking for test execution
• Complete reporting and analysis
• Easy connection with development tools

Understanding TestRail’s Pricing Structure

TestRail has different pricing plans. They are designed for teams of any size and budget. TestRail knows that each team has unique needs. This is why they provide several options with various features and user limits. Teams can find a choice that works for them, fitting their budget and still gaining access to important functions.

For people who are new to test management, TestRail’s Cloud-based plans are a good place to begin. These plans have key features and enough room for small to medium teams.

Plus, they are reasonably priced. Bigger companies with more complex needs can look at TestRail’s Server plans. These plans give more control, customization, and the chance to grow when needed.

All plans include a free trial. This lets teams explore TestRail’s great features and simple design before making a payment. It also shows that TestRail trusts the value of its platform and wants to be open about its pricing.

TestRail’s Customer Reviews and Ratings

TestRail receives many positive comments in customer reviews and ratings. This shows it is user-friendly and has all the features needed for test management. Users appreciate its simple design, making navigation and learning quick. This good feedback helps TestRail remain a top choice for test management. It demonstrates that it can meet the diverse needs of QA teams everywhere.

Customers enjoy TestRail because it has excellent reporting and analytics tools. It can produce detailed reports, custom dashboards, and helpful metrics. This helps teams to make good choices based on data. By doing so, they can keep improving their testing over time.

TestRail stands out not only for its features but also for its great customer support. Users like how fast the support team answers their questions. They also feel that the well-organized documents help make their experience better. This shows that TestRail really cares about keeping its customers satisfied.

3. Qase

Qase is a test management tool that changes how teams work together. It is simple to use and connects easily with other tools. This makes testing easier for everyone involved. The collaboration features help development teams communicate better. Better communication leads to a quicker testing process.

Qase provides real-time updates on test execution progress. This allows team members to coordinate more effectively. With its strong features for test case management and bug tracking, Qase promotes teamwork. It also helps create a culture of quality assurance. Using Qase in your testing activities can boost your team’s productivity and improve overall quality.

Essential Features of Qase

Let’s explore the key features that make Qase a great option for teams needing a reliable test management platform. Its user-friendly interface and powerful reporting tools make testing easier and more organized.

One great thing about Qase is that it makes creating and managing test cases very easy. The test case templates are ready to help you write cases the same way each time.

This reduces the chance of making mistakes. You can quickly find specific test cases with the strong search and filter tools. Plus, you can group them into test suites or test sets to stay organized.

Qase has a version control system that helps keep your work secure and tidy. This system allows team members to track the changes made to test cases over time. It is helpful when multiple people are working on one project. This way, everyone can access the latest version of each test case.

• Workspaces that help teamwork feel easy
• Easy management of test cases
• Changeable templates for test cases
• Quick reporting and analysis
• Connections with popular development tools

Qase Pricing Options

Qase understands that teams need pricing options that match their needs. Whether you are a small startup watching your budget or a large company with different needs, Qase has a plan that fits your budget and helps with cost management.

One great thing about Qase’s pricing is its generous free plan. This plan allows up to five users to use basic features. It’s a fantastic start for small teams or new users of test management tools. As your team grows, Qase offers affordable paid plans with flexible user options. You only pay for what you need.

Qase believes that having clear and simple prices is important. Their pricing page provides all the information you need about the features and limits for each plan. This makes it easier for you to make a good decision. You can find a plan that fits your budget and testing needs.

Real User Reviews on Qase

Qase is dedicated to providing a great test management experience. Many reviews from real users support this. Users appreciate the user-friendly interface and useful features of the platform. They share genuine feedback showing how Qase simplifies testing and improves teamwork.

Many people think Qase is simple to use. It has a straightforward design. Its helpful documents and quick customer support make it a favorite among QA teams of all sizes and skills. Users enjoy its clear appearance, easy workflow, and simple navigation. They feel it aids in completing even the toughest testing tasks.

Users say that Qase has good teamwork features. You can share test cases, assign tasks, and chat with others in real-time. This helps to make the quality assurance process better. It makes everything easier and more efficient.

4. PractiTest

PractiTest is a helpful tool that makes testing easier. It covers all aspects of testing, including planning, running tests, and creating reports. This tool lets you manage your time and resources effectively while testing.

Its user-friendly design allows teams to work together on test cases easily. PractiTest also provides detailed reports and lets you customize fields for better analysis, which helps you make smarter decisions. It works well with popular tools and comes with advanced features. Because of this, PractiTest is a top choice for development teams. It helps increase both testing efficiency and quality.

Core Functionalities of PractiTest

PractiTest is a great choice for QA teams. It has some key features that make it strong and flexible:

1. Comprehensive Test Management Tool: PractiTest provides a complete tool for test management. It handles test planning, running tests, tracking, and reporting.
2. Flexible Test Case Design: The platform allows you to create test cases that are thorough and reusable. You can include custom fields, attachments, and steps to ensure every test is covered.
3. Seamless Integrations: PractiTest connects easily with popular bug trackers, automation tools, and CI/CD tools. This helps support continuous integration and simplifies workflows. You will have clear visibility from start to finish.
4. Powerful Reporting and Analytics: You can make detailed reports, customizable dashboards, and gain valuable insights. These features help you monitor your progress, find issues, and make good decisions.
5. Advanced Risk Management: You can focus on potential risks in your software development process. This helps you address key problems quickly.

Pricing Models for PractiTest

PractiTest has several pricing plans. These plans meet the different needs and budgets of QA teams. Their subscription options include a good mix of key features and price. This makes them a great choice for both startups and bigger companies. It’s important to think about your testing needs and budget. This way, you can choose the best plan for your team.

You can quickly view their prices on the website. It lists the features of each plan. Teams can select either cloud-based or on-premise options, depending on what they require. PractiTest’s pricing makes it easy to scale. You can upgrade or downgrade your plan as your testing needs change.

You might think about trying the free trial. It allows you to explore how PractiTest works before deciding on a plan. This practice helps you understand the features. You can check if it matches your team’s work style and see if it offers good value for your money.

Customer Insights on PractiTest

PractiTest gets positive feedback from its users. This shows it works well as a test management solution. People like the platform because it has an intuitive interface, strong features, and good reporting tools. These things make it a favorite for QA teams that want to improve their testing.

A market study shows that many people want test management solutions that focus on teamwork, visibility, and detailed reporting. PractiTest is exceptional in these areas.

Many customers like using PractiTest because it makes their work easier. It helps teams work better together, especially in agile and DevOps environments. The platform connects easily with popular development tools. It also provides real-time updates on testing progress. This greatly boosts its good reputation.

PractiTest cares about its customers. They offer fast support and listen to feedback to improve their product. This attention to customers has created a loyal base of users. It has also helped the platform build a good reputation in the market.

5. Testmo

Testmo makes it simple to run and manage test cases. It promotes teamwork and fits well with modern software development. With many test management features, like exploratory testing, Testmo enhances overall quality by making testing activities easier.

Its clear design and effective tracking of test execution progress make it a strong choice for development teams. Testmo is setting a high standard for improving testing efforts for better results.

Testmo’s Distinctive Features

Testmo stands out from typical test management solutions. It offers several unique features. These features appeal to QA teams looking for a modern and efficient platform.

1. Intuitive Interface: Testmo has a simple and clean look. This makes tough testing tasks much easier. Users, whether they are technical or not, can navigate the platform and find what they need without trouble.
2. Flexible Test Case Management: Testmo lets you create, organize, and manage your test cases easily. It supports both manual and automated tests. With custom fields and easy editing, your test case documents become clearer. This helps improve efficiency in the data center.
3. Real-Time Collaboration Features: Enjoy smooth teamwork with tools that allow everyone to work together in real-time. Shared workspaces, in-app notifications, and discussion threads keep everyone in the loop. This helps the whole team work better during the testing process.
4. Actionable Reporting and Analytics: Create helpful reports and view your test data using customizable dashboards. You can track key metrics, see trends, and get insights. This will boost testing efficiency and improve overall software quality.

Subscription Plans for Testmo

Testmo offers several subscription plans. These plans are designed for the needs and budgets of QA teams. Whether you have a small team just starting with test management or a big company needing a full solution, Testmo has choices for you. They strive to make their platform user-friendly for all. At the same time, they ensure high quality and good functionality.

Testmo has a free plan for teams that need to save money. This plan allows three users to use the main features. You can try the platform’s intuitive interface. This way, you can see if it works well for your team before choosing a paid plan.

As your testing needs grow, Testmo offers several pricing plans. These plans give you more user space, advanced features, and improved support. This allows the platform to expand with you while fitting into your budget.

Evaluating Testmo Through User Reviews

Testmo quickly received positive reviews from users. This helped it become well-known as a modern and helpful test management solution. It works with several test automation frameworks, including Tricentis qTest, which offers ready-to-use test steps.

QA teams like this platform because it has an easy-to-use interface, smooth integrations, and excellent reporting tools. This brings satisfaction to customers. When choosing a test management tool, think about features, ease of use, pricing, and customer feedback.

Users frequently commend Testmo for simplifying difficult testing tasks. They appreciate its clear layout, straightforward process, and inviting design. The platform is easy to learn.

This helps teams adapt quickly and take advantage of its features to improve their testing efforts. Many reviews point out the increased efficiency from Testmo’s test case management, smart automation, and strong reporting tools.

Testmo pays attention to what customers say. They use feedback to make improvements to the platform. This helps Testmo get better. They update the platform often, add new features, and have a friendly team that works hard to create a good user experience. By focusing on customers, Testmo builds trust and loyalty, making it more popular.

Conclusion

In conclusion, choosing the right test management tool is very important for improving your QA processes. Each tool has its own advantages. You can try Testomat, which has many features. TestRail makes tasks easier.

Qase focuses on teamwork. PractiTest can make your work more efficient, while Testmo offers modern solutions. Think about what you need and read user feedback. Find a tool that fits your team’s goals and budget. Make a smart choice for the best test management tool for your organization to succeed in 2025.

The post Best Test Management Tools 2025: Top Picks & Reviews appeared first on testomat.io.

Let’s consider how we can maximize the most out of this approach,

The integration of a unified approach to BDD, the Test Management System, and Jira — three pillars establishing a clear structure for the testing process, that, in turn, facilitates communication among all project participants.

This suite of tools offers numerous benefits at its core:

→ First, it improves reporting visibility, which minimizes the risk of errors and ensures the delivery of high-quality software.
→ Second, by reducing testing time, it basically is the team’s efficiency; this set of tools leads to accelerating product development.

Further, we delve into detail on how they combine and work

Jira Role in BDD Implementation

Jira is a widely used project management tool for working with requirements and tracking defects. It plays a crucial role in our BDD-oriented approach by serving as a central hub for managing requirements, and it is a well-known tool for team participants that does not require additional training.

Before you pull a user story from Jira backlog into development, it is essential to have a conversation to clarify and confirm the acceptance criteria that your requirements should meet. Some teams do this during Scrum sprint planning, poker sessions, discovery workshops or backlog refinement meetings. Other teams have a specific Three Amigos Meeting. Whatever you call this conversation, its goal looks like an Example Mapping.

The conversation involves the following main iterative stages:

1. The team selects a small upcoming system update from the Jira backlog.
2. They discuss specific examples of new functionality.
3. These examples are pre-documented in a Given-When-Then form, a consistent basis for automated verification.

The result is clarification that serves as a common reference point for the team, enhancing understanding and ensuring alignment on feature requirements. Ensure fast iterations and the ability to make changes as needed, teams can return to earlier stages whenever additional information is needed.

Whereas Jira out-of-the-box does not support documenting user stories in a structured Gherkin syntax, we need to use extra Jira plugins like Xray, Zephyr, or Testomat.io to expand Jira’s capabilities of what we explore further below.

Test Management System and Its Role in BDD Process

Thus, thanks to the integration of Jira and Test Management Plugins, the team implements the pre-described behavior to Given-When-Then user stories. However, the right chosen test management tool can provide much more.

The Test Management System (TMS) is an important tool for organizing and optimizing the software testing process. It allows for:

• convenient planning
• executing and tracking test cases
• analyzing testing results

The test management system fosters more effective interaction among all participants in the process:

• developers (Devs)
• testers (QAs)
• Business stakeholders
• Product Owner
  …. e.g.

In the context of Behavior-Driven Development, a test management system simplifies the verification of BDD scenarios. At the same time, it ensures:

• clear and convenient test representation, Given-When-Then feature file
• ensuring seamless transparency and traceability between features and defects
• real-time documenting and updating with Living Documentation
• control over all verification stages
• streamlining BDD workflow
• maintaining BDD test scenarios

Moreover, Jira BDD and Test Management combination enables faster identification of errors during the requirements formulation stage, when fixing them is least costly — thus ensuring high product quality.

BDD Test Automation Frameworks

Automation scenarios using the Gherkin format help add value to your automation and respond quickly to feedback. Once user stories are described in Gherkin language, they are subject to verification by test automation frameworks. The Given-When-Then statements should be valid during checks with code-based statements defined in the automation framework.

Among the popular Automation frameworks which involve BDD tools and Jira in testing workflow are:

• Cucumber – usually executes test scripts written in JavaScript, TypeScript, Java or Ruby.
• Behave – helps automate BDD Python tests.
• JBehave – a Java-based BDD framework that supports automating of Java step definitions.
• Serenity – extends the capabilities of tools like Cucumber and JBehave. It is Java-based test automation frameworks, also Kotlin and Groovy.

Integration automation with BDD framework into your test workflow provides incredible efficiency for the test process. Namely, this allows:

Make testing not so consuming for repetitive tasks
Reduce overtime risks spent on testing in product releases
Ensure a transparent development process for the whole team
Improve overall software quality.

Integrating BDD, Test Management, and Jira

Now it is time to look at how the combination of BDD, test management tools, and Jira work together and how their connections promote collaboration, transparency, and automation at all stages of the development and testing.

Shortly:

Tests written with BDD are typically formulated in natural language, making them accessible to all participants in the development process.
BDD reduces the distance between development and testing, ensuring requirement compliance.
How Jira ensures that the requirements always align with the tests, quick bug fixes.
TMS allows for better control over the entire development process and timely delivery of a high-quality product.

Understanding of Jira Plugins

Many QA teams integrate Jira into their workflow through third-party services. Jira plugin – ensures integration of BDD tests with tasks in Jira, improving collaboration between testers, developers, and business analysts.

There are approaches to integration testing in Jira:

• Direct connection through Jira plugins, numerous of which you can find on the Jira Marketplace.
• Use of test management systems that support integration with automation tools.

The most common solutions are TestRail, Testomat.io, XRay, Zephyr, and qTest.

Testomatio for Jira

Advanced Jira Plugin is designed to meet one of the key needs of modern development: ensuring collaboration between technical and non-technical specialists in a project. Its advantage is that it is not limited by Jira functionality like many competing solutions. Its Jira integration is bidirectional. Any changes made in the Jira project, including changes to test plans, are immediately reflected in testomat.io, and vice versa – you can work with tests directly in the bug tracking system. Thanks to this, developers and other business stakeholders can fully participate in testing while working in a familiar tool.

The use of solutions like testomat.io test management plugin allows for combining manual and automated testing, including BDD within a single environment, providing convenient test management directly in Jira.

BDD tests can be added to the test management system in 4⃣ ways:

• Creation manual BDD tests – in the Gherkin editor, this is appropriate when the test scenario contains new steps. Intellegent autoformatting and autofilling are provided.
• AI BDD scenario generation – AI analyses your BDD project, existing BDD step, its requirements (Jira tickets) and suggests new ones based on them. If they’re okay, need to approve them.
• Migration and automatic conversion tests to BDD – you can import manual test cases from other TMS, XLSX, or CSV files from other test management systems and automatically convert test cases into BDD scenarios. The system automatically recognizes steps and adds steps in Gherkin syntax to the database for future use. It’s convenient for teams that previously worked without BDD.
• Import automated BDD tests from source code – after creating a new project, you can use the Import Project from Source Code feature, selecting the appropriate parameters (framework, project language, OS). All test scenarios from BDD frameworks will be automatically transferred to the TMS. Testomat.io easily integrates with the popular BDD framework Cucumber. You can import ready-made test scripts from this environment, edit them in TMS, run them, and track test results through detailed reports.

To optimize the process of creating and managing BDD tests, testomat.io offers a range of useful features:

• Reusing steps with the Step Database feature allows you to store all imported or manually created steps, helping save time when creating test cases and keep your project more unified.
• Automatic step updates during synchronization – the step database is updated every time new scenarios are added. If this does not happen automatically, the “Update Steps” function can be used.
• Functional file editing mode – allows you to modify Gherkin scripts: format them, add new or existing steps, and quickly find and replace words in all test steps.
• Tags – is an effective mechanism for managing test sets, allowing them to be grouped by various criteria, including test type or operating system.
• Search and filtering of tests – the ability to find necessary test cases by tags, test status (manual/automated, disabled, etc.), responsible specialist, or other parameters.
• Labels and custom fields – a tool for adapting the TMS to a specific project. It allows assigning labels to test cases, test sets, test plans, etc.
• Conversion of manual tests to automated – after creating manual BDD tests, they can be automatically converted to an automated format. Just write the code and import it into the system.
• Living docs – BDD test scenarios automatically become the technical documentation of the project, available to all participants and updated in real time.
• Advanced analytics – allows analyzing testing metrics such as unstable tests, slow tests, automation coverage, tag statistics, etc.

Create Jira tasks from TMS and track test results and defect fixes without the need to switch between tools.

Integration with Jira allows for executing test cases without technical skills. The bidirectional interaction between TMS and the project management system ensures automatic reflection of test results in Jira and quick creation of defect reports. Look at the picture below, what kinds of test execution you can choose on Jira Plugin Dashboard:

Benefits Automating BDD Testing with Test Management Systems & Jira

Automating BDD testing using test management systems (TMS) and integration with Jira significantly improves software testing efficiency. It ensures continuous testing, reduces testing time, and minimizes errors related to human factors, which is a key element in modern software development processes and allows teams to focus on improving product quality.

Integrating BDD scenarios with Jira allows each scenario to be automatically linked to the corresponding task or user story in the project management system. This helps clearly track which requirements are being tested and which testing stages have been completed.

When integrating BDD tools, such as Cucumber with Jira, testing can be automatically triggered with each change in Jira tasks. For example, when a developer finishes a task or creates a new branch in the repository, the corresponding BDD test scenarios are automatically executed, providing quick feedback and identifying potential defects early in the development process.

One of the main advantages of integrating BDD with TMS and Jira is the automatic generation of test result reports. After executing automated tests, all results can be viewed directly in Jira or in the TMS, enabling teams to quickly identify issues, defects, or deviations from expected outcomes. This reduces the time spent analyzing test results and speeds up decision-making.

Detailed reports help in the faster detection and correction of errors.

Thanks to the integration of BDD with continuous integration and delivery (CI\CD) systems, test automation becomes part of the daily workflows. Every change in code can automatically trigger tests, ensuring continuous testing and minimizing the risks of defects during development. Jira can display the status of tests and allow for real-time tracking of any errors.

Integrating automated tests with Jira keeps test status information up-to-date for all project participants, including developers, testers, and managers. This improves communication and fosters closer collaboration between teams. Everyone can easily review the current state of testing, enabling quick issue identification and process adjustments.

Finally, automating testing with BDD, TMS, and Jira not only accelerates testing but also provides greater reliability and accuracy of results

Xray

Xray test management in Jira – manages all tests as standard Jira tasks, allowing you to customize screens, workflows, and add custom fields for test scenarios.

Xray allows you to plan, write, and execute tests, as well as create detailed test reports. This system uses Jira issues to process tests that change at each stage of testing.

Xray organises tests, allowing you to manage the entire execution process. This ensures tracking of coverage, error detection, and basic release management. Detailed tracking reports allow you to identify which test failed and at which stage the issue occurred. This helps understand what needs to be fixed and efficiently collaborate with developers to resolve the issues. Implemented integration with frameworks like Cucumber and JUnit, so you can coordinate automation testing for your codebase.

What do you get with Xray?

• BDD support – work with behavior-driven testing without any limitations.
• Jira experience – testing is fully integrated into the familiar environment.
• Bug detection – check web, mobile, and desktop applications with new effective methods directly from your desktop, using seamless integration with Xray and Jira.

Xray is one of the oldest test management tools on the market, and some customers admit that its UI and core are somewhat obsolete. The tool works very slowly when dealing with large projects. The downside is that the implementation of large-scale and complex test projects is difficult without failures. It creates test cases with every team-run execution. Additionally, Xray’s advanced functionality is only available in the premium version.

Zephyr

Zephyr can be part of a BDD pipeline, but it doesn’t provide first-class BDD support natively, like before we talked about Xray or Testomat.io

1. Manually written BDD scenarios in Zephyr are not well-optimized for structured Gherkin input or Living Documentation.
2. Automated test cases written in Gherkin can be maintained in version control (e.g., Git), executed with automation tools, and the results pushed back to Zephyr via APIs only.

Zephyr offers users a choice of three options based on team size

• Zephyr Squad is a tool for running manual and automated tests in Jira, the most popular test management Jira test management solution.
• Zephyr Scale is a platform for growing teams working on multiple projects simultaneously, offering features for test result analysis, test run planning, and integration with BDD and CI tools.
• Zephyr Enterprise is an enterprise-level solution that allows testing across the entire organization, integrating the work of multiple teams in a single project, regardless of the development methodology used.

Overall, Zephyr for JIRA is known for its ease of use. However, the downside is that it works slowly and requires payment for each user. As a result, it becomes significantly more expensive than Testomat.io. Moreover, compared to it, Zephyr offers more limited functionality.

Benefits BDD, Test Management & Jira Integration

The integration of BDD with a test management system and Jira significantly improves the testing efficiency. Now let’s briefly recap the main points we have talked about above much

With a unified BDD, Test Management, and Jira integration, you can:

• Extend Jira capabilities, which works only with requirements.
• Formulate user stories and scenarios to define the acceptance criteria for specific software features.
• Create a common language (team communication) for developers and stakeholders (business analysts) to discuss user needs and software solutions.
• Ensure clear and structured test scenarios using natural language (Gherkin).
• Perform traceability of requirements. Link BDD tests with requirements and track progress as test coverage of the project progresses.

Conclusion

The integration of a unified BDD approach, testing systems, and Jira enables efficient tracking of tests and requirements, enhances collaboration between technical and non-technical project participants, and allows for quick response to changes and error correction during SDLC (Software Development Life Cycle). This combination ensures optimization of reporting, reduction of product time to market, and improvement of software quality.

The advantage of TMS is that it is a centralized system that can provide more, in addition to working with requirements, testomat.io also includes importing automated tests and its synchronisation with manual tests. In other words, the Testomat.io Jira plugin brings new capabilities to Jira that it could not offer on its own.

If you have any questions about implementing Jira BDD best practices together with our test management solution, drop us a line without hesitation!

The post Integration between BDD in Jira and Test Management System appeared first on testomat.io.