How to set up QA processes from scratch [2026]

A well-defined QA process helps teams test software consistently, catch defects earlier, and make better release decisions.

It usually includes requirement analysis, test planning, test design, execution, defect reporting, regression testing, and release validation. Each stage supports the next. Weak requirements can reduce test coverage, while poor defect tracking can make re-testing unreliable.

A clear QA process also defines what needs to be tested, who owns each activity, which risks need more attention, and how product quality should be measured over time.

Before getting into the QA process itself, let’s start with what quality means in software and how QA contributes to it.

What is Quality?

In software development, quality means delivering an application that is functional, reliable, user-friendly, and defects-free.

Achieving quality involves continuous testing, monitoring, and improvement throughout the software development lifecycle. It ensures that the final product provides value to end-users, meets business goals, and adheres to technical and user requirements.

What is QA Process?

A Quality Assurance (QA) process is the set of activities a team follows to check whether software meets its requirements and is ready for release. It defines how testing is planned, carried out, documented, and reviewed throughout development.

The process typically covers requirement analysis, test planning, test case design, test execution, defect management, regression testing, and release validation. It also defines who owns each activity and what conditions must be met before testing or release can move forward.

How is QA different from QC?

Quality Assurance (QA) and Quality Control (QC) are both essential for ensuring high product quality, but they focus on different aspects of the process.

Below is a comparison between the two:

QA focuses on preventing defects early by improving processes, while QC identifies defects later in the production cycle. Both are necessary to ensure high-quality standards.

Why is QA Process important?

A QA process gives teams a consistent way to manage quality throughout development instead of relying on testing only at the end.

Its main value comes from reducing uncertainty. Teams know what needs to be tested, which risks matter most, how defects should be handled, and what conditions a build must meet before release.

A defined QA process also helps teams:

• Catch defects earlier: Reviewing requirements and testing continuously reduces the chance of major issues appearing late in the release cycle.

• Improve test coverage: Structured planning makes it easier to identify missing scenarios, edge cases, and high-risk areas.
• Reduce rework: Defects found earlier are usually simpler to fix and less likely to affect multiple parts of the product.
• Make releases more predictable: Clear entry and exit criteria give teams better visibility into whether a build is ready to move forward.
• Maintain consistency across teams: Shared processes, documentation, and quality criteria reduce differences in how individual testers or projects approach testing.
• Support better quality decisions: Metrics such as defect leakage, test coverage, and reopen rate show where the process is working and where it needs attention.

Risks of not having a QA Process

Without a defined QA process, testing often depends on individual judgment rather than a shared method. That makes coverage uneven and increases the chance that important issues are discovered only after release.

The main risks include:

• Higher defect leakage: Missing checks and inconsistent coverage allow more defects to reach production, especially in less obvious paths and edge cases.
• Unclear ownership: When responsibilities are not defined, teams may assume someone else has tested a feature, verified a fix, or checked release readiness.
• Inconsistent testing: Different testers may use different approaches for similar features, which makes results harder to compare and gaps harder to spot.
• More rework: Defects found late often affect more code, test cases, and dependent features, increasing the effort needed to fix and verify them.
• Unpredictable releases: Late failures and repeated re-testing can delay releases because teams do not have clear quality criteria or reliable test status.
• Poor visibility into quality: Without metrics such as defect leakage, test coverage, and reopen rate, teams have little evidence to show where quality is improving or declining.
• Loss of user trust: Production defects in critical flows can affect reliability and user experience, especially when similar issues continue to appear across releases.

Stages in QA Process

A QA process is made up of several connected stages. Each stage produces inputs for the next one, so problems introduced early can affect the rest of the testing cycle. For example, unclear requirements can lead to incomplete test cases, while weak defect reporting can slow down re-testing and release validation.

1. Requirements Analysis

QA should be involved before test execution begins. At this stage, testers review functional requirements, business rules, user flows, acceptance criteria, and non-functional expectations such as performance, security, and usability.

The aim of requirements analysis is to identify what needs to be validated and where the requirements leave room for interpretation. Testers should look for missing conditions, conflicting requirements, unclear expected behaviour, and dependencies that could affect testing later.

This stage also helps determine risk. A change to a payment flow, authentication system, or data migration path usually needs deeper validation than a minor UI update. Identifying that risk early helps the team decide where more test effort is required.

2. Test Planning

Once the requirements are understood, the team defines how testing will be carried out.

The test plan usually includes the scope of testing, test levels, testing types, environments, timelines, responsibilities, and required resources. It should also state what is out of scope so there is no assumption that every part of the product has been validated.

Teams should also define entry and exit criteria. Entry criteria specify what must be ready before testing starts, such as a stable build, available test data, and configured environments. Exit criteria define what must be true before testing can be considered complete, such as critical tests passing and high-priority defects being resolved.

Automation decisions also belong here. Instead of automating everything, teams should identify repeatable, stable, and frequently executed scenarios that can provide long-term value through automation.

3. Test Design and Preparation

The next stage turns requirements and risks into actual tests.

Testers create test cases, test scenarios, checklists, test data, and automation scripts. Tests should cover expected behaviour as well as invalid inputs, boundary conditions, alternate paths, error handling, and dependencies between features.

Test data needs the same level of attention. A test may be correctly designed but still miss a defect if it only uses simple or ideal data. Teams may need different user roles, account states, transaction histories, permissions, locales, or data volumes to represent meaningful conditions.

The test environment should also be prepared at this stage. Where possible, its configuration should be close enough to production that environment differences do not hide defects or create failures that would not occur for users.

4. Test Execution

During execution, testers run the planned tests and compare actual behaviour with expected results.

This is not only about marking tests as passed or failed. A failed test needs to be investigated before it is treated as a product defect. The failure may come from application code, test data, an unavailable dependency, an incorrect environment configuration, or an unreliable automated test.

Execution may happen at several levels. Developers may perform unit testing, while QA validates APIs, integrations, user interfaces, end-to-end flows, performance, security, and other areas included in the test scope.

Results should be recorded consistently so the team knows what has been tested, what remains blocked, which failures are under investigation, and whether coverage is sufficient for the current build.

5. Defect Tracking and Management

When testing exposes a defect, the issue needs to be documented in a way that developers can reproduce and investigate.

A useful defect report should include the affected feature, steps to reproduce, expected and actual behaviour, environment details, supporting logs or screenshots where needed, and the severity or impact of the issue.

The team then tracks the defect through its lifecycle. A reported defect may be confirmed, prioritized, assigned, fixed, re-tested, reopened, or closed.

Prioritization matters here. A defect that prevents checkout or causes data loss carries a different release risk from a minor visual inconsistency. Severity, user impact, frequency, and available workarounds should all influence how the issue is handled.

6. Re-Testing and Regression Testing

After a defect is fixed, QA first re-tests the original scenario to confirm that the reported problem has actually been resolved.

That alone is not enough. A code change can fix one problem while introducing another in a related area. Regression testing checks whether existing functionality still behaves correctly after the change.

The regression scope should depend on the impact of the fix. A small isolated change may require targeted regression, while changes to shared services, authentication, payment logic, or core APIs may require a much broader suite.

Teams should also pay attention to repeated failures during regression. If the same area regularly breaks after unrelated changes, it may point to fragile code, missing automated coverage, or tightly coupled components.

7. Release Validation

Release testing checks whether the final build is ready to move into production.

Teams commonly begin with smoke testing to verify that the build is stable enough for deeper validation. They may then run targeted regression tests based on the features changed, defects fixed, and risks identified for that release.

Release readiness should not depend only on the percentage of tests that passed. Teams should also look at unresolved defects, affected business-critical flows, blocked tests, known limitations, and whether required quality criteria have been met.

A build with a high pass rate can still carry unacceptable risk if an unresolved defect affects login, payments, data integrity, or another critical function.

8. Process Review and Improvement

QA does not end when a release goes live. Teams should review what happened during the testing cycle and use those findings to improve the next one.

Useful signals include escaped defects, frequently reopened bugs, areas with repeated regression failures, flaky automated tests, blocked test cases, gaps in requirement coverage, and delays caused by environment or test data problems.

The point is to trace those outcomes back to the process. If defects regularly escape from the same feature, the issue may be weak test design or missing coverage. If testing is repeatedly delayed, the real problem may be unstable environments or late requirement changes.

These findings should feed back into requirement reviews, test planning, automation priorities, and release criteria. That feedback loop is what turns QA from a series of testing activities into a repeatable quality process.

Common Problems in QA Process

When establishing the quality assurance testing process, a few issues may come up:

1. How and where to store test documentation?

• Each quality assurance testing professional usually works on multiple projects simultaneously. In that case, it’s essential to securely categorize and store test data and documentation.
• Initially, something like Google Drive works, but as the number of projects and team members expands, management will be better off transferring the information to a more professional test management tool.

2. How to train new QAs?

As a QA department grows, newly hired personnel must be trained and informed about the processes, ongoing projects, and other data relevant to the team.

• Handle this by creating some knowledge base (perhaps a cookbook) that would contain how the particular quality assurance testing team works, what tools it uses, common bottlenecks, best practices, and the QA flow followed on projects.
• Once a new QA engineer joins, they can find all the information in one convenient location.
• However, remember to keep adding new information as QA processes keep evolving.

3. How to ensure comprehensive testing?

While working on multiple projects, testers may miss out on testing certain features or functionalities, thus allowing bugs to pass into production. To prevent such omissions, create a practical software testing checklist that details exactly what to test, check for, and verify.

4. How to account for personnel changes?

For example, a QA is working on a big project and needs to go on leave in the middle of it. The project has to be transferred to another QA engineer who might not have any context on the project or the software structure.

• Instead of going through the entire project every time someone new comes on board, it’s easier to create a map detailing the structure and flow of each project.
• If someone new is involved in a project, they must refer to this map to understand the product and the methods required to test it.
• Through user management, access controls, and version controls of the documents, this problem can be managed well.

How to set up QA processes from scratch?

Here is a step-by-step guide to help you set up a QA process for the first time.

1. Analyze Requirements

It costs more to fix a bug detected during testing than prevent them at the stage of requirements design. QA professionals should be involved in analyzing and defining functional and non-functional software requirements. QAs must be offered consistent, comprehensive, traceable requirements and marked. This helps the QA team design tests specifically tailored to the software being tested.

2. Plan the tests

The information gained during the requirements analysis phase is used for test planning. The test plan should comprise the software testing strategy, the scope of testing, the project budget, and established deadlines. It should also outline the types and levels of testing required, methods, and tools for tracking bugs and allocate resources and responsibilities to individual testers.

3. Design the tests

QA teams must craft test cases and checklists encompassing the software requirements at this stage. Each test case must contain conditions, data, and steps to validate each functionality. Every test must also define the expected test result so that testers know what to compare actual results to.

• It is recommended that QAs start with a measure of exploratory testing to familiarize themselves with the software. This would help with designing appropriate test cases.
• If an automation strategy has been defined in the test scope, this is the stage for creating automation testing QA scenarios.
• This is also the stage for preparing the staging environment for execution. This environment should closely mirror the production environment regarding hardware, software, and network configurations.
• Other characteristics like databases and system settings should also be closely mimicked.

4. Execute Tests and Report Defects

Tests start at the unit level, with developers performing unit tests. Then, the quality assurance testing team runs tests at API and UI levels. Previously designed test cases run manual tests. All bugs detected are submitted in a defect tracking system for effective defect management.

• Time-travel through every test with access to every log. Fix faster with Smart Failure Analysis, which learns from your testing and identifies the causes of test failures.
• Detect and fix flaky tests with intelligent suggestions or mute them to avoid false alarms.

5. Run Re-Tests and Regression Tests

Once bugs have been found, submitted, and fixed, QAs test the functions again to ensure they didn’t miss any anomalies. They also run regression tests to verify that the fixes have not affected the existing functions.

6. Run Release Tests

Once developers issue a release notification detailing a list of already implemented features, fixed bugs, recurring issues, and limitations, the QA team must identify the functionalities affected by these changes. Then, the team must design modified test suites that cover the scope of the new build.

The QA team must also perform smoke tests to ensure each build is stable. If the test passes, modified test suites are run, and a report is generated.

Set up the QA process for Agile Environments

In an Agile environment, QA cannot operate as a separate phase that begins after development is complete. Testing needs to happen within the sprint, alongside design and development, so teams can identify issues while the work is still in progress.

That changes how the QA process is planned and executed.

1. Involve QA During Sprint Planning

QA should participate when stories are being discussed, not only after they are assigned for testing.

During refinement and sprint planning, testers can review acceptance criteria, identify missing scenarios, question ambiguous requirements, and estimate the testing effort involved. This is also the right time to identify dependencies such as APIs, test data, environments, third-party services, or device requirements.

For example, a story to add a new payment method may look small from a development perspective. QA may identify additional coverage around declined payments, retries, saved cards, refunds, currency handling, network interruptions, and existing payment methods.

Finding those scenarios before development starts gives the team time to account for them within the sprint.

2. Define What “Done” Means for Testing

A story should not be considered complete simply because development has finished.

Teams should define quality conditions as part of their Definition of Done. Depending on the product, these may include:

• Acceptance criteria have been validated
• Required automated tests have passed
• Relevant regression tests have been executed
• Critical and high-priority defects have been resolved
• Required browser, device, or platform coverage has been completed
• Test evidence and defect status have been documented

These conditions make release decisions less subjective. They also prevent unfinished testing work from repeatedly carrying over into later sprints.

3. Test Within the Sprint

Testing should begin as soon as a testable part of a story becomes available.

QA does not always need to wait for the complete feature. APIs can be tested before the UI is ready. Individual components can be validated before an end-to-end workflow is complete. Test data and automation scripts can also be prepared while development is still underway.

This shortens the feedback cycle. If a problem is found while the developer is still working on the feature, it is usually easier to investigate and fix than an issue discovered several stories or sprints later.

The goal is to keep development and testing close enough that defects do not accumulate near the end of the sprint.

4. Use Risk to Decide Testing Depth

Agile teams work within short delivery cycles, so every story cannot receive the same level of testing.

QA should use risk to decide where deeper validation is required. Consider factors such as:

• Business impact if the feature fails
• Number of users affected
• Complexity of the code change
• Dependencies on other services or components
• History of defects in the affected area
• Whether the change touches shared functionality
• Difficulty of recovering from a production failure

A text change may only need targeted validation. A change to authentication, checkout, permissions, or shared APIs may need integration testing, negative scenarios, broader regression coverage, and additional platform testing.

Risk-based testing helps teams use limited sprint time without treating every change as equally important.

5. Build Regression Testing Into Every Sprint

Frequent releases increase the chance that a new change affects existing behaviour. Regression testing therefore needs to be part of the sprint rather than an activity saved for a major release.

Start by identifying stable, business-critical flows that should be checked repeatedly. Good automation candidates usually include login, checkout, core navigation, account management, and other workflows that are executed frequently and are unlikely to change every sprint.

Not every regression test needs to run at the same time. Teams can use a smaller suite for pull requests or individual builds and run broader regression suites before release.

The suite itself should also be reviewed. Tests that no longer provide useful coverage, duplicate other tests, or fail frequently because of poor design increase maintenance without giving the team better confidence.

6. Keep Defect Feedback Short

A defect that moves through several handoffs can consume a large part of a short sprint.

QA and developers should communicate directly when a failure is unclear or difficult to reproduce. Defect reports still need proper steps, evidence, environment information, and expected behaviour, but not every issue needs a long asynchronous discussion before investigation starts.

Once a fix is available, QA should re-test the original failure and decide the regression scope based on what changed. A fix in a shared component may require much more regression coverage than the original defect suggests.

7. Review Quality at the End of the Sprint

Sprint reviews and retrospectives should consider quality signals alongside delivery status.

Look beyond the number of completed stories. Teams should examine questions such as:

• Which defects escaped initial testing?
• Which stories required repeated rework?
• Were tests blocked by unstable environments or missing data?
• Did automation failures slow down feedback?
• Were acceptance criteria changed after development started?
• Did regression testing expose unexpected dependencies?

These patterns tell you more about the health of the QA process than a simple pass rate.

For example, repeated defects caused by misunderstood requirements may call for stronger QA involvement during refinement. Frequent regression failures in the same module may point to fragile code or insufficient automated coverage.

Agile QA works best when these findings are carried into the next sprint instead of being treated as problems belonging only to the release that exposed them.

Tools Required for Quality Assurance Testing

QA teams usually need more than one tool because different parts of the QA process solve different problems. The right toolset depends on what you test, how often you release, and how much of the process is automated.

The main categories include:

1. Test Management Tools

Test management tools help teams organize test cases, test runs, requirements, and results in one place.

They are especially useful when several testers work across multiple releases or products. Instead of storing cases in separate documents, teams can track which scenarios were executed, which ones failed, and which requirements still lack coverage.

Look for capabilities such as:

• Test case organization and versioning
• Test run planning
• Requirement traceability
• Defect linking
• Reporting and dashboards
• Integration with issue trackers

These tools become more important as the test suite grows and manual tracking becomes difficult to maintain.

2. Test Automation Tools

Automation tools are used for repeatable tests that need to run frequently.

Tools such as Selenium, Playwright, Cypress, Appium, and Puppeteer support different types of browser and application testing. The right choice depends on the application, supported platforms, programming language, and test architecture.

Automation works best for stable scenarios such as regression flows, API checks, and frequently executed business-critical paths. Tests that change constantly or require human judgement may still be better suited to manual testing.

Automation tools should also fit into the wider delivery pipeline so teams can run tests on pull requests, builds, or scheduled regression cycles.

3. Bug Tracking Tools

Bug tracking tools record defects from discovery through resolution.

A useful defect record should include the steps to reproduce the issue, expected and actual behaviour, severity, environment details, supporting evidence, and current status.

Tools in this category help teams assign defects, set priorities, track progress, and link issues to releases or test cases.

The tool itself does not improve defect quality. Teams still need consistent reporting rules so developers receive enough information to investigate failures without repeated back-and-forth.

4. API Testing Tools

API testing tools validate service behaviour without depending on the user interface.

Tools such as Postman, SoapUI, and Rest Assured can be used to check request and response data, status codes, authentication, error handling, schema validation, and response time.

API testing is particularly useful when:

• The UI is not ready
• Several applications depend on the same backend service
• Business logic is handled mainly through APIs
• Teams need faster feedback than end-to-end UI tests can provide

API tests are generally faster and less fragile than UI tests, which makes them useful for continuous integration.

5. Performance Testing Tools

Performance testing tools measure how an application behaves under different levels of load.

Tools such as JMeter, LoadRunner, and Gatling can help teams test response time, throughput, concurrency, and system behaviour when traffic increases.

Performance testing should be tied to measurable requirements. For example, instead of stating that a page should load quickly, define the expected response time under a specific number of concurrent users.

These tools are useful for identifying bottlenecks in APIs, databases, infrastructure, and other system components before those limits affect users in production.

6. Continuous Integration Tools

CI tools such as Jenkins and CircleCI allow teams to run automated checks whenever code changes are introduced.

QA teams can use CI pipelines to trigger unit, API, integration, UI, and regression tests at different stages.

Not every test should run on every code change. Faster checks can run on each commit or pull request, while longer regression suites can run before release or on a scheduled basis.

The pipeline should give developers quick feedback without making every change wait for large test suites unnecessarily.

7. Cross-Browser and Device Testing Tools

Web and mobile applications may behave differently across browsers, operating systems, screen sizes, and device types.

Cross-browser testing tools help teams validate these differences without maintaining every possible environment locally.

Coverage should be based on actual product requirements and user data. Testing every available browser or device usually adds little value. Teams should prioritize combinations that represent important user segments, known compatibility risks, and critical business flows.

Choosing the Right QA Toolset

A larger toolset does not automatically produce better testing. Every additional tool introduces setup, maintenance, training, and integration work.

Choose tools based on the problem they need to solve. A team with a small application and short regression suite may not need the same infrastructure as a team releasing across several platforms every day.

The most useful QA toolset is one that supports the existing process, gives the team reliable test results, and keeps enough visibility across planning, execution, defects, and releases.

QA Process Models and Frameworks

QA process models and frameworks provide structured approaches for integrating quality into every stage of software development.

They define when testing happens, how teams collaborate, and what level of rigor is required to prevent defects effectively. Understanding these models helps teams choose the right approach based on project complexity, delivery speed, and organizational maturity.

Waterfall Model

In the Waterfall model, QA begins only after development is complete. Testing follows a linear sequence, making this approach suitable for projects with stable, well-defined requirements. While predictable, it offers limited flexibility to adapt to changes discovered late in the cycle.

V-Model Testing Model (Verification and Validation Model)

An evolution of Waterfall, the V-Model pairs each development phase with a corresponding test phase. For example, requirement analysis aligns with acceptance testing, and design aligns with integration testing. This model emphasizes early test planning and improves defect prevention, though it still requires stable requirements.

Agile QA Model

Agile Testing model integrates QA throughout iterative development cycles. Testing occurs continuously inside each sprint, with QA collaborating closely with developers, product owners, and designers. This model supports rapid delivery, early detection of defects, and faster feedback loops, making it ideal for evolving requirements.

DevOps QA Model

DevOps extends Agile by combining development, QA, and operations into a unified pipeline. Automated testing, continuous integration, continuous delivery, and continuous monitoring ensure quality at every stage—from commit to deployment to post-release. This model emphasizes speed, reliability, and a shared responsibility for quality.

Shift-Left Testing and Shift-Right Testing

Shift-left testing model focuses on moving QA activities earlier in the lifecycle—starting quality checks during requirements, architecture, and design.

Shift-right complements this by validating quality in production through monitoring, real-user feedback, feature flags, and chaos testing. Together, they expand QA beyond traditional pre-release activities.

Hybrid and Customized Models

Many teams use a hybrid approach, blending elements of Agile, DevOps, and shift-left strategies to fit their workflow. These customized models allow organizations to adapt QA intensity, automation levels, and collaboration practices based on their product scale and delivery goals.

These frameworks help teams establish clear workflows, align quality with development pace, and ensure a predictable, repeatable process for delivering reliable software.

Metrics & KPIs to be measured for QA Process

QA metrics help teams understand whether testing is finding defects early, covering the right areas, and supporting stable releases. The numbers are most useful when you track them over time and investigate why they change rather than treating a single value as a quality score.

Key QA metrics and KPIs include:

• Defect Density: Measures the number of defects found in a module, feature, or defined unit of software. A consistently high defect density in one area can point to complex code, frequent changes, unclear requirements, or insufficient testing. It is most useful for comparing areas within the same product rather than unrelated applications.
• Defect Leakage: Tracks defects that are discovered after release instead of during pre-release testing. Rising leakage may indicate missing scenarios, weak regression coverage, environment differences, or incorrect risk prioritization. Teams should also consider defect severity since one escaped payment failure may matter more than several cosmetic issues.
• Defect Removal Efficiency (DRE): Shows what percentage of known defects were caught before release. It can be calculated as defects found before release ÷ total defects found before and after release × 100. Track DRE across multiple releases since a high value for one release does not necessarily mean the QA process has improved.
• Test Coverage: Shows how much of the intended testing scope has been validated. Coverage may refer to requirements, features, APIs, user journeys, platforms, or code. A high percentage does not guarantee effective testing, so teams should also review which high-risk paths remain uncovered.
• Requirements Traceability Coverage: Measures whether requirements and acceptance criteria have corresponding tests. Gaps can reveal functionality that has been implemented without clear validation. Traceability is particularly useful when requirements change because teams can quickly identify which tests may need to be updated.
• Test Execution Rate: Compares the number of tests executed with the number planned for a test cycle or sprint. A low rate can indicate limited testing time, blocked environments, unavailable test data, unstable builds, or unrealistic planning. The reason tests were not executed is usually more useful than the percentage itself.
• Pass/Fail Rate: Shows how many executed tests passed or failed. It gives a quick view of build stability but should not be used alone for release decisions. A 98% pass rate can still represent serious risk if the remaining failures affect login, checkout, payments, or another critical flow.
• Mean Time to Detect (MTTD): Measures how long it takes to discover a defect after it is introduced. A high MTTD can indicate that important problems are being found only during late regression or release testing. Earlier API, integration, or CI checks can help shorten this feedback cycle.

• Mean Time to Resolve (MTTR): Tracks how long defects take to move from discovery to resolution. Long resolution times may come from development effort, but they can also result from weak defect reports, unclear ownership, environment delays, or repeated re-testing. Breaking MTTR down by stage helps identify where the delay actually occurs.
• Automation Coverage: Indicates how much of the relevant test scope is covered by automated tests. Higher automation coverage is useful only when the automated tests are reliable and protect scenarios worth repeating. Stable regression tests, critical user flows, and API checks are generally more valuable automation candidates than frequently changing scenarios.
• Reopen Rate: Measures how often defects marked as fixed fail verification and need to be reopened. A high reopen rate can point to incomplete fixes, misunderstood requirements, weak impact analysis, or insufficient developer testing before the fix reaches QA.
• Release Defect Rate: Tracks defects reported after a release and helps teams compare release stability over time. Review the metric by severity and affected area rather than relying only on the total count. Repeated production defects in the same feature can reveal a persistent gap in requirements, test coverage, or risk assessment.

These metrics should also be read together. For example, high test coverage with rising defect leakage can mean that many tests are running but important scenarios are still missing. Increasing automation coverage with frequent flaky failures can indicate that the suite is growing without providing more reliable feedback. Looking at these relationships gives teams a clearer view of where the QA process actually needs improvement.

Best Practices for QA Process

A strong QA process should help teams find important defects earlier, reduce unnecessary rework, and make release decisions with better evidence. The practices below focus on keeping QA useful as the product and release cycle grow.

• Define clear quality criteria: Decide what a feature or release must meet before testing begins. This can include acceptable defect severity, required regression coverage, performance thresholds, supported platforms, and unresolved risks. Clear criteria reduce subjective release decisions later.
• Prioritize testing based on risk: Do not give every feature the same testing depth. Consider business impact, technical complexity, user frequency, dependencies, past defect history, and the cost of failure. High-risk areas such as authentication, payments, and permissions usually need broader coverage than low-impact UI changes.
• Involve QA early: Bring QA into requirement reviews, refinement, and planning. Testers can identify missing acceptance criteria, unclear business rules, dependency risks, and difficult test conditions before development is complete. This reduces the chance of discovering basic requirement gaps during execution.
• Keep requirements and tests traceable: Link important requirements and acceptance criteria to the tests that validate them. This helps teams spot missing coverage and understand which tests need updates when a requirement changes.
• Automate where repetition provides value: Prioritize stable tests that run frequently, such as smoke tests, API checks, critical regression paths, and data-driven scenarios. Avoid automating tests simply to increase an automation percentage. High-maintenance tests can add more noise than value.
• Treat flaky tests as defects in the test suite: A test that fails unpredictably weakens confidence in every automated result. Investigate causes such as synchronization issues, shared test data, unstable environments, poor selectors, or external dependencies instead of repeatedly rerunning the test until it passes.

• Use representative test environments: Test environments should match the production conditions that can affect application behaviour. Pay attention to configuration, permissions, databases, integrations, feature flags, browsers, devices, and network conditions. Differences that matter should be documented rather than discovered during a production incident.

Conclusion

A QA process gives teams a consistent way to plan testing, manage defects, and assess release readiness. It works best when QA starts early, testing is based on risk, and teams use metrics to spot gaps in coverage and recurring issues.

As the product and release cycle evolve, the QA process should evolve with them while keeping testing focused on the risks that matter most.