“If it ain’t broke, don’t fix it,” the old adage goes. But things do break, and they need to be fixed, updated, modified or tweaked frequently, especially in the world of software. And when changes are made there’s a risk that rather than making things better, something else gets broken.
Software applications are complex and interconnected. They’re also fragile and imperfect and patches and fixes will need to be applied regularly. This means that as soon as version 1.0.1 of a software product exists, QA teams must carry out regression testing.
And getting regression testing wrong can be calamitous. Already this year, we’ve seen Volkswagen and Porsche have to recall more than half a million vehicles in the US due to a rearview camera glitch, while Volvo too have also needed to recall hundreds of thousands of cars for urgent updates for a rearview camera issue.
There’s an irony at play here that the failure of these companies to look back over their work properly meant that so many drivers couldn’t look backwards properly, but that’s not to make light of the serious consequences of getting regression testing wrong. Last year, a buggy app update even cost the Sonos CEO his job. HP was involved in years of litigation over ‘bricked’ printers. But getting regression testing right is complicated, with experienced QA professionals required to make trade-offs and sound judgments.
Regression testing isn’t just for mature systems
Regression testing starts well before the first release. Even in early development, product owners expect to see constant improvement rather than old bugs resurfacing. But the level of risk is significantly raised once the software is in the wild. As soon as people depend on a product for their daily work, any update that breaks existing functionality becomes a major liability.
This is not a failure of process or discipline on the part of software developers, but an intrinsic property of software itself. The way modern systems are built revolves around reuse and centralization. Shared libraries, common services, and abstracted infrastructure mean development is faster and maintenance is easier. When a bug is fixed in one place, the benefits are seen across the system.
Unfortunately, the same is true when a bug is introduced. A change made to address a specific issue can break functionality in parts of the application that are seemingly completely unrelated. Engineers will insist that they touched only a small, isolated component – and they’re usually telling the truth. The problem is that in software systems very few things exist in complete isolation.
Regression testing isn’t about making sure new features work
This is why regression testing exists. Its purpose is not to prove that the software is perfect or even that new features work particularly well. It is simply about ensuring that the software is not worse than it was before. Users who relied on a feature yesterday should be able to rely on it today. New functionality may arrive in a broken or imperfect state, and while this is unfortunate, it rarely causes immediate disruption. What does cause disruption, though, is when features that people previously depended on suddenly stop working.
The need for regression testing is even more critical with updates. Users may be hostile toward them, unconvinced that they are necessary; they may even believe that they’re a way of perpetuating planned obsolescence.
But in reality, updates are usually unavoidable. Regulatory requirements change and security vulnerabilities emerge. Even when an update has been issued solely to fix a security issue, regression testing is essential. A secure system that no longer performs its core functions is not going to please end users, and will damage a company’s reputation.
Developing a robust regression testing strategy
Regression testing isn't a 100% reliable process, but tech companies release thousands of updates on a daily basis, and it's only the faulty ones that get publicity. A single line of code could alter execution paths in ways that are difficult to predict, and to give themselves the best chance of finding any bugs QA teams must work out what they need to test, as well as how they run these tests.
A robust regression testing strategy starts with an understanding of what the system is supposed to do. In an ideal world, this is based on documented requirements. In the real world, where documentation often lags behind deployment, this 'backbone' is often built on empirical observation – a shared understanding of established behavior. Whether the source is a formal specification or the collective 'tribal knowledge' of the team, these expectations must be captured in test cases to ensure that yesterday’s progress doesn't become tomorrow's regression.
And maintaining this backbone is harder than it sounds. Real users often interact with systems in ways those who created them hadn’t anticipated. Sometimes bugs even become features for certain users. And in fixing these bugs, QA teams may then realise they had become essential for some workflows. The line between defects and features is blurred, meaning any decisions about what should and shouldn’t regress are complicated.
Running regression tests on everything isn’t realistic
There will always be an attempt to predict the impact of changes without running extensive tests. Static analysis tools map out code paths, and in recent years AI-enabled solutions can attempt to infer behavioral consequences from source code changes. These tools are helpful, but don’t solve the problem as such; while they provide additional insights, they cannot give any certainty. Ultimately, regression testing remains the only reliable way to detect when software has become worse than it was before.
Running exhaustive tests where every single possible use case is checked is not realistic, so even the most comprehensive regression test suites will have gaps. In an ideal world, every single change would involve a full cycle of regression testing. In practical terms, though, this is an expensive process, requiring a great deal of maintenance effort on the part of QA teams, and a lot of computation.
So organizations make assumptions; they trust that recent fixes are sufficiently targeted and do not invalidate everything that has already been tested. These assumptions aren’t always correct, but without making them, most software would take a long time to ship.
Regression test libraries need regular maintenance
Running regression tests is only part of the challenge for QA teams; the test suites also need to be maintained. Manual testing doesn’t scale well and is prone to human error. Automated scripts are quick, but also fragile; user interfaces change, workflows evolve, and suddenly large portions of the test suite fail. Teams are then forced to decide whether to invest time fixing scripts and slow down the testing process or to reduce coverage and increase risk levels.
Newer approaches such as agentic and AI-assisted testing can address some of this fragility. Instead of rigid scripts that expect exact UI layouts, intelligent agents can interpret intent and adapt to changes in presentation. This reduces fragility, but also comes with a certain level of uncertainty. These systems are improving rapidly, yet they still require human oversight. For now, they complement traditional regression testing rather than replace it.
Regression testing: The least-worst option
Winston Churchill once famously described democracy as “the worst form of government, except for all those other forms that have been tried from time to time”. The same could be said for regression testing. It is rarely perfect, it is often expensive, and it can be frustratingly slow. Yet it remains the only viable defense we have against the inherent instability of changing code.
In the wake of a major software failure, hindsight usually reveals a missed opportunity – a specific test case that, had it been run, would have caught the issue. This is the central tension of the discipline: while no suite can catch every bug, almost every bug was catchable in theory.
Instead of seeing these failures as purely negative, experienced teams view them as essential data. Every production defect is an opportunity to refine the backbone of your testing strategy. By feeding these lessons back into the regression suite, the software gets more resilient. We don't perform regression testing to achieve perfection; we do it to ensure that the mistakes of the past are never repeated in the future.
But the ultimate truth is that once software is used in the real world, regression testing is essential. After the first release, the question is no longer whether change will introduce problems, but when. Regression testing is how teams detect those problems before users do, and that alone makes it one of the most critical disciplines in software production.
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