Why Your QA Engineer Should Be the Most Stubborn Person on the Team

There is a common stereotype that software testing is just a dull exercise in checking what should already work. In reality, the cost of a missed bug in a serious product is far higher than a minor visual glitch or a button shifting out of place. It can lead to failures in critical workflows, data loss, service outages, and major financial damage for the business.

People often say that QA is just there to check developers’ work. That is a superficial view. The role of QA is not simply to confirm that the code works, but to try to uncover every scenario in which it can fail. QA engineers have to think differently from the people who built the system. They need to think like real users, including those who will inevitably follow unexpected paths or use the product in ways no one originally planned.

The myth that QA is routine work has lasted for years. Yes, regression testing is part of the job. But if you force an engineer into rigid checklist-driven testing, you will miss bugs. Strong QA does not follow a script mechanically. It explores the product, looks for edge cases, and finds new ways it can fail that no one considered during planning.

QA Tasks and Approaches

In most organizations, there is a distinction between QA (Quality Assurance) and QC (Quality Control), and both need to be considered together. It is not enough to verify the quality of the product being shipped. Teams also need to analyze the development and testing process itself, identify weaknesses, and look for ways to improve the product over time.

Tasks break down into two major streams:

• Bugfix and regression. It is not enough to confirm in a ticketing system that a developer has fixed a bug. The goal is to make sure it does not come back a month later. That is why any critical fix should be covered by a regression test. This becomes a safeguard against product degradation.
• Feature testing. The work starts long before the build. Documentation and requirements are studied not just to understand how the feature is supposed to work, but to anticipate how it might break neighboring components. Depending on complexity, the right testing approach is chosen: from simple checklists to full test cases and automation where it makes sense.

Good teams aim for high automation coverage, but they also stay realistic. Test coverage varies across modules: legacy code and experimental features require different approaches. In some areas, developer-owned unit tests may be enough. In others, QA needs to validate functionality through more complex integration scenarios.

What Good QA Looks Like and How to Build It 

Good QA means stable tests that provide sufficiently complete coverage of a feature or product. It is also critical that QA and development teams actively collaborate. For example, when handing a product off to development, QA provides reports on what was tested and how, what risks remain, and what can be improved in coverage and overall product quality.

It is also good practice to write tests alongside the code. For example, developers may write TAP-based functional tests for PostgreSQL or unit tests for Go code. At the same time, QA engineers prepare to test the finished product: they analyze documentation, talk to business analysts and technical product managers, and clarify expected behavior.

Based on that information, QA specialists create test cases and checklists, automate them when appropriate, and add them to CI.

It also helps to have a DevOps engineer who can take infrastructure work off QA’s plate. If there is no DevOps engineer, that responsibility often falls to the QA engineer as well.

Manual vs. Automated Testing

Automation is invaluable for repetitive tasks where manual re-checking leads to attention fatigue. In these cases, automated tests improve test quality and reduce errors caused by human fatigue or inattention.

Exploratory manual testing complements automation. It helps uncover product behavior, edge cases, and unusual scenarios that automated tests may not cover.

When building automated tests, test design techniques can be applied deliberately. For manual testing, the engineer's own experience matters most: you need to look at the product broadly and systematically, analyze what affects its behavior, and try to break it from angles nobody thought to consider.

For example, one of my colleagues once tried to bring up a cluster in a nonstandard way by skipping one step, and the system failed. A user may follow their own path, and the developer simply may not have accounted for it.

From an exploratory testing perspective, it is valuable to have custom tooling for fuzzing and generating random input combinations. For example, an in-house combinator can shuffle files with SQL queries to produce unexpected execution paths and input combinations.

ASan is commonly used for detecting memory errors, while Valgrind is widely used for memory debugging, leak detection, and profiling. For web application testing, Selenium Grid supports cross-browser execution at scale. For automated tests, Pytest can serve as the test execution framework, while Allure Report generates CI-integrated reports from the test results. TestRail handles test case management and coverage tracking at the team level.

Testing Against Open Source and Third-Party Components

QA becomes more complex when a product is built on top of an open-source core or depends heavily on external components. In that case, the team is not testing only its own code. It is also testing how upstream changes, extensions, integrations, and internal modifications behave together.

For example, a product based on PostgreSQL may include its own commercial logic while still pulling changes from the upstream project. That changes the QA process. If a bug is found in upstream code, quietly patching it only inside the commercial product is not always the right approach. In many cases, the issue should be reported back to the community, especially when the defect affects the original project and not only the commercial layer.

The same logic applies to extensions and external components. Before adding a third-party component, QA should treat it as untrusted until it has proven stable in the target environment. That means exploratory testing, compatibility checks, regression coverage, review with SCA tools, and verification under realistic workloads. If bugs are found, they should be reported to the maintainers, and the component should be added only after the fix is available and the behavior is stable enough for production use.

Upstream bugs are often harder to investigate than internal code defects. With internal development, the team usually has full visibility into every commit. If something breaks, tools such as git bisect can quickly point to the change that introduced the regression. With upstream code, changes often arrive in larger batches. When something fails after a merge, QA and developers may need to analyze a much larger set of external changes before they can isolate the root cause.

Communication works differently, too. With internal code, the developer who owns the change may be one message away. In an open-source project or a third-party component, the feedback loop is longer. Reports need to be reproducible, technically precise, and useful to maintainers. This is where strong QA becomes more than bug reporting. It becomes engineering communication.

AI and Vibe Coding: Hype or Genuine Value?

Vibe coding is having a moment: you describe a task to an AI, and it writes the code for you. In QA, this can work, but with some important caveats.

The most practical current use cases are generating sets of basic test cases (for example, pairwise combinations) and writing small helper scripts. This helps remove the “blank page” problem and saves engineers from some routine work.

But it is important to understand the boundary. AI is like a junior engineer who never gets tired but often hallucinates. It cannot reliably anticipate deep architectural specifics of a product or subtle edge cases. At some point, a human must step in. Otherwise, the vibe can end with a critical bug in production.

How to Become an Effective QA Engineer and Grow in the Profession

At university, testing is typically covered as part of a programming curriculum. That is enough to learn the basics and understand whether the field interests you. After that, you can continue learning independently or take dedicated QA courses.

For many serious QA roles, strong Python skills are one of the most useful foundations for automation, along with a solid understanding of Linux. This is especially true in systems development: you simply cannot test a complex product effectively if the terminal intimidates you or you do not understand how the environment works.

But hard skills alone are only tools. The key soft skills for QA are systems thinking and the ability to make a clear technical argument. Finding a bug is not enough. You also need to explain why it should be fixed and make the case to the developer. This is not about avoiding conflict. It is about clear, constructive communication in a shared technical language.

Where Can QA Engineers Grow?

1. Technical track: QA Architect or Lead SDET. This path means continuous growth in coding, test architecture, and CI/CD. You become the expert who can build a quality engineering infrastructure from the ground up. 
2. Management track: Team Lead or Head of QA. Here, the focus shifts to processes, hiring, and mentoring. The idea that managers do not need deep technical knowledge is dangerous. To lead a team of engineers, you need to understand their pain points and the complexity of their work.

And yes, some QA engineers eventually move into development or analysis. Testing experience becomes a real advantage there: engineers with a testing background tend to write cleaner code because they can see in advance where it is likely to break.

Useful Resources

A short list of resources to help you get started in QA and stay current with what matters:

• How Google Tests Software by James Whittaker, Jason Arbon, and Jeff Carollo: A useful book on how testing can be organized at scale, with strong ideas around risk, ownership, automation, and engineering culture. 
• Lessons Learned in Software Testing by Cem Kaner, James Bach, and Bret Pettichord: A foundational text covering practical testing wisdom that holds up regardless of stack or methodology.
• ISTQB: Not every strong tester needs certification, but ISTQB is very useful for learning shared testing terminology, test design basics, and the difference between QA, QC, verification, validation, test levels, and test types. ISTQB materials are especially helpful for beginners who need structure. 
• Ministry of Testing: One of the best-known global testing communities, with articles, discussions, courses, events, and practical material on quality engineering, exploratory testing, automation, and modern QA careers. 
• Test Automation University: A practical resource for learning automation, API testing, UI testing, CI integration, and related engineering skills.

Final Thoughts

QA requires a lot of patience and focus, but knowing when to switch context matters just as much. Sometimes that is exactly what helps you solve a problem after spending a week chasing an intermittent failure with no clear result.

A solution can almost always be found. Do not forget physical activity or anything else that lets your brain step away from the task for a while. It gives you the energy to come back and look at the problem from a different angle.