Software Regression Testing: Enhanced Regression Strategy and Zero Defects

Zero Defects

The term “Zero Defects” is a QA/QC concept that aims to reduce and minimize the number of bugs and issues in a process and to do things right the first time. The main idea is to prevent mistakes from occurring rather than correcting them after they occur. This concept was first introduced by Philip Crosby in his 1979 book “Quality is Free.”

The main point of zero defects is not about achieving perfection, but about setting a standard of implementing mechanisms to meet those standards consistently. Zero defects is not a specific process with defined steps, but a mindset or dev/tech culture that needs to be embraced throughout a team/company. It involves continuous improvement processes, recognizing the high cost of quality issues, and working proactively to identify and fix bugs in apps and dev processes.

The concept of achieving zero defects remains more of an ideal than a reality in real project cases. Instead, the focus shifts towards minimizing defects that have an impact on business/users/systems, especially the ones that are critical enough to break app functionality. This approach underscores the importance of prioritizing quality from the project's outset to mitigate costly errors down the line.

Regression Testing

1. Costs - identifying and fixing defects early saves costs by avoiding business impact on production.
2. Test cycle - automation of regression tests reduces time and resources, providing fast metrics on product quality.
3. Requirements - regression testing may uncover missed requirements, ensuring broader software coverage.
4. UX - high-quality software leads to increasing customer satisfaction.
5. Risks - Over time, you’ll be sure that no occasional bugs are in the legacy parts of the software.

Scope

1. Prioritize test cases (tests in checklists) to optimize testing resources and time without sacrificing the quality.
2. Prioritize high-risk features to find and fix critical/major defects earlier.
3. Test particular code directly affected by recent changes instead of the entire app. Conduct only necessary tests/types of tests.
4. Update the regression test suites/cases/checklists when needed.
5. Validate fixes for specific issues - do not replace with regression testing.
6. Retest the entire app after each major change, code refactoring, or tech stack update.

When

1. After patching defects in the current version of the app.
2. After changing features due to changed requirements.
3. After adding new features or functionality to the app.
4. After changing the configs of the app and infra.
5. After fixes/changes based on performance and security testing issues.
6. After changing used software/libs/frameworks versions or hardware.

Effective Regression Testing

1. Analyze changes in business requirements, software architecture, code, envs, and test scope.
2. Plan how, when, and for how long regression testing will be performed, plan iterations and consequences, and have best-case and worst-case scenarios.
3. Automate test cases when possible and needed and cost-effective for faster testing.
4. Specify when and why to start and end regression testing.
5. Prepare the test envs and test data sets, execute test cases, record bugs, and retest bug fixes iteratively.
6. Communicate regression testing results and conclusions to stakeholders, dev, and QA teams.
7. Review and analyze the effectiveness of the approaches and processes and identify areas for improvement for future testing iterations.

Enhanced Regression Strategy

CI/CD Integration

Autotests/scripts triggered automatically with each new build/commit/staging deploy ensure that changes/fixes are tested and validated. Such integration brings a culture of continuous improvement and quick results - teams can detect and address issues/bugs early in the SDLC. It helps to deliver high-quality software at a faster pace, introducing agile methodologies processes.

Test Data

Ensuring the availability of diverse/realistic test datasets improves test coverage and validation of app features. Using data generation tools (customized or self-written) or obfuscated prod (real users’) data for testing can improve the creation of effective test scenarios. Using data masking/obfuscating protects sensitive info during testing, ensuring compliance with data protection and security policies.

Exploratory Testing

Combining or replacing regression testing with exploratory testing may improve test coverage and uncover unusual regression defects. QA engineers can use their domain knowledge and intuition to explore the app to find “hidden” issues/bugs that may have been missed in regression (autotests) tests. This agile combined approach helps teams to find complex tricky issues and corner cases that standard regression tests may easily miss.

Performance & Security Testing

Combining performance and security testing with functional regression testing is important for not missing issues with app performance and security. These are standard for your app performance and security test, but they are performed as a regression test in a case when significant changes are made and app performance may be affected or new vulnerabilities might be introduced in your system.

Cross-Browser Testing

Using cross-browser (cross-platform/device) testing allows QA engineers to validate app functionality and layout across different browsers, versions, OS, devices (hardware), and screen resolutions. It is important to understand the reasonable scope and perform only necessary tests because this type of testing might be time-consuming, but it might be faster by using platforms like BrowserStack or your own device farm + automation. However, it takes more resources than, for example, API regression or functional regression testing so be careful and optimize the scope according to made changes and proven risks.

Shift-Left

Identify potential regression risks, and plan regression testing activities at the earlier stages of feature implementations/code changes. This early involvement set collaboration between dev and QA teams, minimizing rework, bug fixing, number of testing iterations, additional regression testing, and accelerating time-to-market.

Additional Noteworthy Approaches

Chaos Engineering for Regression Testing

It’s from the field of distributed systems; it involves deliberately introducing controlled chaos into a system to uncover weaknesses and failures. Traditionally, it’s used for resilience testing, but chaos engineering can be adapted for regression testing.

In regression testing, chaos engineering subjects the software to unpredictable and different conditions/data sets similar to prod envs. By intentionally disrupting/changing some components, such as network connections, dependencies, or infra, testers/devs can see how the app responds to unexpected input.

Integrating Chaos Engineering into regression testing processes gives additional ways to identify and fix potential regression risks/bugs before they impact end-users or the testing process in later stages.

Mutation Testing for Regression Tests Analysis

Mutation testing is a technique where small changes are made to the source code to simulate potential bugs. By assessing how well the checklists/test cases detect these changes/bugs, QA engineers can assess the effectiveness of their regression tests. This approach provides info on the effectiveness of the test suite and shows cases/areas where additional changes are needed.

Automated Code Analysis

Tools that use machine learning algorithms to identify the underlying causes of regression defects might be quite useful for regression testing strategy. By analyzing code changes, test results, and system logs, these tools can suggest the source of regressions. This approach speeds up bug prevention and resolution and reduces the time spent on investigation, improving overall productivity and time to market. AI-based tools/algorithms also can analyze code changes and test results (stats) to identify the most relevant tests for execution.