How to Assess Software Test Quality

Don’t Focus Too Much On Writing More Tests Too Soon 📌 Prioritize Quality over Quantity - Make sure the tests you have (and this can even be just a single test) are useful, well-written and trustworthy. Make them part of your build pipeline. Make sure you know who needs to act when the test(s) should fail. Make sure you know who should write the next test. 📌 Test Coverage Analysis: Regularly assess the coverage of your tests to ensure they adequately exercise all parts of the codebase. Tools like code coverage analysis can help identify areas where additional testing is needed. 📌 Code Reviews for Tests: Just like code changes, tests should undergo thorough code reviews to ensure their quality and effectiveness. This helps catch any issues or oversights in the testing logic before they are integrated into the codebase. 📌 Parameterized and Data-Driven Tests: Incorporate parameterized and data-driven testing techniques to increase the versatility and comprehensiveness of your tests. This allows you to test a wider range of scenarios with minimal additional effort. 📌 Test Stability Monitoring: Monitor the stability of your tests over time to detect any flakiness or reliability issues. Continuous monitoring can help identify and address any recurring problems, ensuring the ongoing trustworthiness of your test suite. 📌 Test Environment Isolation: Ensure that tests are run in isolated environments to minimize interference from external factors. This helps maintain consistency and reliability in test results, regardless of changes in the development or deployment environment. 📌 Test Result Reporting: Implement robust reporting mechanisms for test results, including detailed logs and notifications. This enables quick identification and resolution of any failures, improving the responsiveness and reliability of the testing process. 📌 Regression Testing: Integrate regression testing into your workflow to detect unintended side effects of code changes. Automated regression tests help ensure that existing functionality remains intact as the codebase evolves, enhancing overall trust in the system. 📌 Periodic Review and Refinement: Regularly review and refine your testing strategy based on feedback and lessons learned from previous testing cycles. This iterative approach helps continually improve the effectiveness and trustworthiness of your testing process.

If I am helping a team with their testing approach, I want a sense of how they are doing. This is partly to pick where to focus, and partly to assess later whether there has been any improvement. I want to avoid rigid assessments, but I do want something that gives me a sense of "needs work" versus "doing great", and various flavors in between. The main things I want to know are how much does the team all the way through management understand the testing and product quality (Testing Transparency), how well does the testing cover the risks (Testing Quality), and how much is the team observing product after release to improve both testing and the product itself (Issue Escapes). I use a 1-4 numerical scale that is subjective, but which can also be checked with evidence. A 1 means "not at all, or in no meaningful way", 2 means "a small degree but poorly", 3 means "good, but with room to improve, and 4 means "could not imagine better, excellent". One can collect this assessment from interview with team members, and then check if there is actual evidence (documentation, reports, activity) which matches the self-assessment. Each of the three categories has multiple statements. Testing Transparency - The team knows what is going to be tested. - The team knows what was tested and what was discovered. - The team has an assessment of product quality before release. - The team has an assessment of product quality after release. - The team knows what to do to improve testing and product quality. Testing Quality - Testing coverage is described. - Testing covers all of product functional areas. - Testing covers quality categories (security, performance, accessibility, etc.). - Testing is efficient. Issue Escapes - All post-ship issues are traced back to a set of root causes. - Post-ship fixes target systemic causes and prevention rather than one-off fixes. A team in high performance mode is probably going to have 3s or better on most of those questions. A team that is struggling and lost is going to be most 1s and a few 2s. A team making improvements is going to see numbers increase over time. Avoid treating this like a math exercise. One team's 2 might be another team's 3, and an average across all the numbers probably cooks so much subjectivity into the assessment to make the final number useless. Use it instead to orient yourself on where to focus attention, and to remind yourself and the team of any success they have achieved already. #softwaretesting #softwaredevelopment You will find more articles and cartoons in my book Drawn to Testing, available in Kindle and paperback format. https://lnkd.in/gM6fc7Zi

💬 I get this question a lot in interviews: "What quality metrics do you track?" Here’s the basic version of my answer—it’s a solid starting point, but I’m always looking to improve it. Am I missing anything? What would you add? ✨ Engineering Level I look at automated test coverage—not just the percentage, but how useful the coverage actually is. I also track test pass rates, flake rates, and build stability to understand how reliable and healthy our pipelines are. ✨ Release Level I pay close attention to defect escape rate—how many bugs make it to production—and how fast we detect and fix them. Time to detect and time to resolve are critical signals. ✨ Customer Impact I include metrics like production incident frequency, support ticket trends, and even customer satisfaction scores tied to quality issues. If it affects the user, it matters. ✨ Team Behavior I look at where bugs are found—how early in the process—and how much value we get from exploratory testing vs. automation. These help guide where to invest in tooling or process improvements. 📊 I always tailor metrics to where the team is in their journey. For some, just seeing where bugs are introduced is eye-opening. For more mature teams, it's about improving test reliability or cutting flakiness in CI. What are your go-to quality metrics? #QualityEngineering #SoftwareTesting #TestAutomation #QACommunity #EngineeringExcellence #DevOps #TestingMetrics #FlakyTests #ProductQuality #TechLeadership #ShiftLeft #ShiftRight #QualityMatters

Ever noticed how seemingly simple methods can reveal complex truths? In the world of software testing 🧪, mutation testing is often surprising because, despite its simplicity, this technique can uncover tests’ worthiness. It can, for example, be used to measure the quality of AI (or human) generated test suites or help to generate high-quality ones. It’s not just about measuring code coverage, which some consider as a proxy/vanity metric, but about truly challenging our test suites and verifying their integrity. Here’s what you need to know: ‣ 𝐌𝐮𝐭𝐚𝐭𝐢𝐨𝐧 𝐓𝐞𝐬𝐭𝐢𝐧𝐠: is a software testing technique used to evaluate the quality of a “test suite” by intentionally introducing small errors, or "mutations", into the code and then checking if the test suite can detect these mutants by causing the tests to fail. The idea is that if the test suite is thorough, it should be able to catch these introduced mutants. ‣ 𝐌𝐮𝐭𝐚𝐭𝐢𝐨𝐧 𝐎𝐩𝐞𝐫𝐚𝐭𝐨𝐫𝐬: are methods to create and choose mutants. This is a well-studied field, yet leading methods, in many cases, are practically inefficient since it could be very challenging to generate meaningful, diverse and realistic mutants. This is one place where AI could be a game-changer! ‣ 𝐌𝐞𝐚𝐬𝐮𝐫𝐢𝐧𝐠 𝐓𝐞𝐬𝐭 𝐈𝐧𝐭𝐞𝐠𝐫𝐢𝐭𝐲: comparing the original test suite on the original code vs mutants. The more mutants it kills (i.e., failing tests on mutants that pass on the original), the stronger the test suite. Generating high-quality code or tests isn't just about calling a smarter LLM; it is also about exploiting code-specific techniques and integrating them with LLM inferences in a well-engineered flow. For more on this topic, see, for example, Cover-Agent open-sourced tool by CodiumAI #codecoverage #mutationtesting #AI