The software testing trends for the near future fall into three areas: AI both as something to test and as a testing tool, professional test data management, and a whole-team view of quality under growing regulatory pressure. What matters most is that quality is no longer a job for testers alone but a shared team responsibility, which takes social skills and genuine collaboration.
Key Takeaways
- AI as a testing tool only makes sense when there is a concrete problem for it to solve. A tool that solves nothing is useless, no matter how big the budget.
- Test data management will matter even more in the future because of regulations such as the Cyber Resilience Act and mandatory accessibility requirements.
- Non-functional quality characteristics such as performance, usability and security have to be part of the design from the start, not something checked in a final test.
- Static code analysis is an automatic code review tool that is available right now and helps pay down technical debt, yet many teams still barely use it.
- Software quality only works as a team effort: testers who know the whole product and actively shape communication are better placed for this than any single role.
Which Software Testing Trends Should Testers Really Watch?
Among all software testing trends, AI comes first, but not for its own sake. For testers, it raises two questions: How do you test AI, and how can AI help with testing? Both are young fields, unlike the way we have handled classic software for decades.
Testing AI brings an old question back with new urgency: What does quality mean in this context? The classic tester pattern of test case in, expected result out often doesn’t work here. Other methods are needed, statistical approaches for example. Anyone working in software development should at least have a rough idea of this, even without being an AI expert.
The second question, AI as a testing tool, calls for a sober look. AI is a tool. The question is which concrete problem you want to solve, not which tool to install because there happens to be an AI budget up for grabs. If AI solves a problem, go for it. If the tool doesn’t help, leave it.
“If there’s a problem I can solve with AI, go for it. But just installing some tools that don’t bring any benefit gets you nowhere.”
(Richard Seidl)
There are plenty of sensible places to start: understanding requirements better or making them more complete, working through specifications more cleanly, generating better test cases, supporting automation. Those are legitimate uses. Playing around with Copilot is only justified if it actually makes development faster or better.
Test Data Is Here to Stay
Many companies struggle with test data management, and that won’t change any time soon. AI brings the topic back in a new form. Test data specialists will still be in demand for years to come.
On top of that, regulation is raising the pressure: the Cyber Resilience Act, usability requirements, mandatory accessibility. All of this forces teams to think about quality more broadly than before.
Build Quality In Instead of Checking It at the End
The split between developers and testers no longer works. Even in an agile team, it isn’t enough for one person to write the code and toss it over to someone else who tests it afterwards. Quality has to be part of the whole process.
This applies to non-functional testing in particular. If you only run a penetration test, a performance test or a usability test at the very end and find problems there, you force costly rework late in the project. Suddenly people are asking whether the framework fits or whether the architecture is the right one. That’s a can of worms nobody wants to open that late.
Thinking about these non-functional topics during design saves corrections later. Testers can contribute a lot here, both for what users see and for what happens inside, such as maintainability. Many companies run software that is 10, 20 or 30 years old, with mountains of technical debt that just get shifted back and forth. New software shouldn’t be allowed to get to that point.
Why Software Testing Fails Most Often in Practice
The most common mistake is having no strategy at all. You don’t need a 40-page test plan, but you do need a rough checklist: Which test levels, which test types, how much test automation, what about static analysis, reviews and test techniques? For each item, you can then decide with a risk-based view whether the project needs it.
Not every project needs full-scale integration testing or fully automated UI tests. You’re allowed to solve things differently. But you have to think it through once. That’s exactly the step that’s often missing.
The second classic problem is the quality of the tests themselves. There are often plenty of test cases, whether unit tests, system tests or business-level tests, and often they’re even automated. Still, there’s room for improvement. With unit tests, people like to chase coverage metrics: 80 percent code coverage is treated as a must. Whether extra test cases with different values would be worthwhile, ones that don’t raise coverage but test better, often gets lost along the way.
Why Static Analysis Is a Quick Win
Static analysis is one of the clearest quick wins in testing, and teams often leave it on the table. The tools have existed for years and effectively give you an automatic reviewer for code and documents.
While everyone is calling for AI, here is a ready-made machine that gives you input for paying down technical debt. The usual excuse that the tool reports too many findings only half holds. Of course you have to work through the results. But you can come up with a strategy that doesn’t hurt. Nobody has to hide away for a year doing nothing but fixing issues. It fits into day-to-day work.
Which Skills Testers Need for the Future, Including with AI
The most important shift isn’t in technical skills but in collaboration. The first idea of agile often was to put a tester on the dev team. The tester then sat through the sprint, got the software dumped on them at the end and started testing. If they found bugs, the sprint review fell apart. That doesn’t work anymore.
Good teams live on soft and social skills. How do people talk to each other, how transparent are they, how do they work together in pair programming or pair testing? That’s where knowledge spreads. Testers start doing a bit of coding, and developers build quality awareness instead of first developing and only then testing.
Traditionally, the tester was the person who knew the whole product and everyone involved, because they had to keep asking questions. That position in the project network makes the role ideal for improving quality across the board and stepping out of the testing silo. Quality starts at the front, with the requirements.
Instead of only reviewing requirements and pointing out flaws, the job is to contribute constructively: helping developers adjust checks in their IDE, building a better code base. Skills count, not roles. If you switch from ivory-tower communication via Jira ticket to looking at things together, you’ll get further.
How Working Side by Side Turns into a Real Team
An example from the financial sector shows what this change looks like. A team switched to agile and immediately asked what would happen to the test team. They put a tester on the team who sat through his sprints and wrote manual test cases in a big old test management tool while the developers worked through one user story after another. It didn’t work at all.
Through retrospectives and working together, the team gradually started talking and sharing more. Working side by side turned into working together. One developer began to think about automation early and to build it himself, drawing on the testing know-how in the team. The tester, who came from a purely business background, got into the technology, helped build test automation and gave feedback on the unit tests.
One morning, a developer and this tester were sitting at one computer doing pair testing, entirely on their own initiative. Nobody had told them to. They had decided by themselves to spend a morning working through the topics together. The divide was gone, and the work was visibly more fun. Moments like that show that things can go better than they often do today.
Mentoring Beats Traditional Consulting
Guidance works better than lecturing. The classic expert consultant comes in and explains how it’s done, and the reaction is often: I’m not listening to you anyway. A coaching approach works better: listening more, offering impulses, using questions to bring topics into the team.
In practice, that means a team isn’t supported full time but in one or two sessions a week. You look together at what’s coming up, offer an impulse, build one improvement and let it run for the week. That way the team climbs a little higher every week.
The real lever is often not where management expects it. “We all need to do test automation” isn’t always the point where a team really needs help. An expensive tool or an open source tool won’t fix everything. The real problems often lie somewhere else. That goes for AI too: in many teams, the problems are so deeply rooted in other issues that AI wouldn’t be a solution and would more likely make things worse.
Mastermind Groups as a Space to Grow
A fixed group of five or six people who want to grow in the same field can push each other forward a lot. Test managers, freelance test automation engineers or testers meet every two to four weeks over a longer period, at a fixed time, and work through their challenges in a structured way.
That takes a high level of trust, so NDAs are part of it, because people bring difficult topics to the table. The topics range from professional ones such as test methodology to personal ones such as burnout, stress and how to talk about it. The group stays together for three to six months, and many carry on longer and keep supporting each other.
Frequently Asked Questions
When is it worth using AI tools for software testing?
Only when there’s a specific problem that AI can solve. An open AI budget isn’t a reason to install tools that don’t provide any benefit. Useful starting points include: better understanding or refining requirements, processing specifications more accurately, generating better test cases, and supporting automation. Even experimenting with a copilot is only justified if it makes development faster or better.
Why are traditional testing methods insufficient for testing AI systems?
The well-established pattern of inputting a test case and getting the expected result often doesn’t work with AI. The first question is what “quality” even means in this context. After that, different methods are needed, such as statistical approaches. The field is young compared to the well-established, decades-long approach to traditional software. Anyone working in software development should have a basic understanding of this without necessarily being an AI expert.
Why should performance, usability, and security be considered as early as the design phase?
Because late findings set the whole project back significantly. If you wait until the very end to run a penetration test, a performance test, or a usability test, you suddenly find yourself questioning the framework and architecture. Taking these non-functional aspects into account early on prevents such corrections. This also applies internally, for example, to maintainability: Many companies run software that’s 10, 20, or 30 years old, with technical debt that’s simply being passed around from one team to another.
Does every project need a comprehensive test plan?
No. A 40-page test plan isn’t necessary, but a rough checklist is: Which test levels, which test types, how much test automation, and what about static analysis, reviews, and test methods? For each point, a risk-based decision can be made as to whether the project needs it. Not every project requires comprehensive integration testing or fully automated UI testing. The most common mistake is having no strategy at all.
Is 80 percent code coverage a good quality goal for unit tests?
The metric alone says little about the quality of the tests. Teams tend to chase after the coverage metric because 80 percent is considered mandatory. In doing so, they overlook whether additional test cases with different values would be useful, ones that don’t increase coverage but provide better verification. Having many test cases, even automated ones, does not necessarily mean the verification is effective.
Is static code analysis worth it if the tools report a large number of findings?
Yes, it’s one of the clearest quick wins. These tools have been around for years and effectively serve as an automated reviewer for code and documents, as well as providing input for reducing technical debt. The excuse of too many findings only holds half the truth: With the right strategy, addressing them can be integrated into day-to-day operations. No one has to spend a whole year holed up fixing bugs.
Is it enough for agile work to simply add a tester to the development team?
No. A team in the financial sector switched to agile, added a tester who sat out the sprint and wrote manual test cases in the old test management tool while the developers worked through user stories. It wasn’t until they began holding retrospectives and working together that a dialogue emerged: the developer contributed ideas for automation, the tester implemented them, and provided feedback on the unit tests.
How do mastermind groups work to foster professional development in the testing field?
Five to six people from the same field, such as test managers, freelancers specializing in test automation, or testers, meet every two to four weeks at a set time and systematically work through their challenges. The group remains stable for three to six months, often longer. Since the topics range from testing methodology to burnout and stress, trust and appropriate NDAs are essential.
