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When the topic is technical debt, the conversation usually gets directed at development teams. “That’s a dev problem.” Fair enough, after all, debt is born in the code. The thing is, QA is almost always the first to feel its effects, before anyone else does. And that puts the team in a strategic position that, when used well, turns complaints into arguments, and arguments into real change.
What is technical debt?
Technical debt is the future cost of decisions made today in the name of speed. Patched code to meet a deadline, a feature shipped without test coverage, a critical flow that was never documented, it all adds up.
Estimates suggest that accumulated technical debt in U.S. software reached $1.52 trillion, with the annual cost of poor software quality exceeding $2.41 trillion (CISQ Cost of Poor Software Quality Report, 2022). On top of that, a 2023 McKinsey study points out that technical debt accounts for roughly 40% of companies’ IT assets (McKinsey Digital).
This isn’t a peripheral problem. It’s the kind of thing that slows down delivery, drives up maintenance costs, and opens security vulnerabilities, sometimes without anyone noticing until it’s too late.
Why does QA feel it before everyone else?
Think about what happens when technical debt grows unchecked: regression tests that break with any minor change, bugs that come back after being fixed, critical user journeys that work one moment and fail the next.
Who catches that? The QA team.
In practice, though, that awareness stays trapped in the technical layer. QA opens the ticket, the dev fixes the symptom, and the underlying issue stays exactly where it was. That cycle repeats until the pile of patches becomes unsustainable.
What changes when the QA team stops just reacting and starts using that data as evidence? The conversation moves to a different level entirely.
What can the QA team actively do to reduce this debt?
- Map the critical flows that have never been automated
Every product has journeys that simply cannot fail, the checkout process, login, account creation, submitting an important form. In many companies, these flows are tested manually, sporadically, by a different person every sprint. That’s not test coverage; that’s wishful thinking.
QA can map those flows, prioritize by business impact, and start automating them systematically. Every automated flow is a piece of the debt being paid off.
- Turn repetitive manual tests into recurring automations
Manual tests that run every week are immediate candidates for automation. Beyond freeing up the team for more strategic work, automation guarantees consistency: the same flow, tested the same way, every single time.
- Use failure patterns as an argument, not just a report
If a specific bug shows up every time a particular module is touched, that’s not bad luck. It’s a sign of something structural. The QA engineer who collects that history and brings the pattern to the development team and management isn’t just filing documentation, they’re delivering intelligence.
- Get involved earlier in the cycle, not just at the end
A large share of technical debt is born because QA enters the process too late, when the code is already written and the deadline is already tight. Taking part in requirements reviews, acceptance criteria definitions, and architecture discussions significantly reduces the number of problems that ever make it to the testing phase.
The biggest obstacle: fragmentation and lack of visibility
Many QA teams still work in silos. One squad uses Selenium, another uses Cypress, a third keeps manual tests in spreadsheets. Reports end up scattered, every tool speaks a different language, and any manager trying to get a consolidated view of quality eventually gives up.
That fragmentation is, in itself, a form of technical debt. Without centralization, there’s no way to spot patterns, prioritize efforts, or demonstrate the real impact of QA work to the people who make decisions.
How intelligent automation changes this picture
Automating tests on its own isn’t enough. What actually makes a difference is automating with consistency, visibility, and resilience, tests that don’t break every time a field gets renamed or a screen gets redesigned.
This is exactly where TestBooster.ai comes in. The platform works as a single place where all of a company’s tests are centralized, scheduled, and monitored in real time.
The reports it generates aren’t purely technical. They’re designed to speak to QA engineers, developers, and managers alike, translating quality data into business impact. Which is precisely the kind of language that gets QA a seat at the decision-making table.
QA as a strategic player
There’s an old and mistaken view that the QA team is just a filter at the end of the process, the department that approves or rejects what development delivers. That framing no longer holds up in any serious team.
A QA team with consolidated data, continuously running automations, and reports showing historical patterns has something far more powerful than the ability to block a deploy. It has the argument to change how software is built in the first place.
Technical debt stops being an informal complaint and starts having a name, a number, a history, and measurable impact. That’s the material you need to have a real conversation with product, engineering, and leadership, and that’s how QA becomes an active part of the solution.
Conclusion
Reducing technical debt isn’t the development team’s responsibility alone. QA is in a privileged position to identify where problems accumulate, document patterns, and drive structural change, as long as it has the right tools and processes to do so.
If your team is still stuck in repetitive manual testing, fragmented reports, and automations that break with every deploy, it’s worth taking a look at TestBooster.ai. The platform was built to solve exactly these problems, and to turn software quality into a real competitive advantage.
