Does AI reduce the need for software testers? Let’s see what the numbers show.
AI coding tools are now part of normal software development. Sonar’s 2026 State of Code Developer Survey found that 72% of developers who have tried AI use it every day, and AI-assisted or AI-generated code now accounts for 42% of committed code. Developers also reported an average personal productivity boost of 35%, with 82% saying AI helps them code faster and 71% saying it helps them solve complex problems more efficiently. The productivity gain is real, as is the shift in how software gets built.
That has led some people to assume AI will reduce the need for software testers. However, the evidence points the other way. The more code AI helps produce, the more code has to be reviewed, challenged, and tested before it can be trusted.
Additionally, faster output does not remove quality risk. In many cases, it multiplies it. Sonar’s survey shows that 96% of developers said they do not fully trust that AI-generated code is functionally correct. That’s significant and is the top view among the developers using these tools.
AI speeds up coding, but it does not prove the code is right
There is a difference between generating code quickly and knowing that the code is dependable. The survey shows that developers are getting value from AI, but they are also seeing its limits. The report found that 61% of developers agree that AI often produces code that looks correct but is not reliable. The same percentage said it takes a lot of effort to get good code from AI through prompting, fixing, and refinement. That means AI is not a replacement for software quality practices. A lot of that work is moving downstream into verification.
That shift matters because code that looks clean can still fail in subtle ways. A response that compiles, passes a quick skim, or appears logically sound can still contain edge-case defects, integration issues, security weaknesses, flawed assumptions, and brittle behavior. The report also found that less than half of developers said AI has had a positive impact on end-user experience or reducing technical debt, even while it certainly improved speed and time-to-market. That is a useful warning for any organization tempted to confuse coding velocity with software quality.
The real bottleneck is verification
If developers broadly distrusted AI-generated code but consistently verified it before commit, the quality risk would be easier to manage. The same survey shows that this is not what is happening. Only 48% of developers said they always check their AI-assisted code before committing it. Nearly all developers spend at least some time reviewing, testing, and correcting AI output, and 38% said reviewing AI-generated code takes more effort than reviewing code written by human colleagues.
That is the real verification bottleneck. AI can help create more output, but teams still need skilled personnel with proven software testing skills who can decide whether that output is fit for production. The report found that when developers were asked what skills will matter most in the AI-assisted coding environment, the top answer was reviewing and validating AI-generated code for quality and security. Even the people benefiting from AI are saying that validation is now one of the key skills in software work.
Why certified software testers matter even more now
This is why certified software testers are needed. ASTQB cites analysis showing that a combination of defect prevention, pre-test defect removal, and formal testing by certified personnel can top 99% defect removal efficiency while also lowering costs and shortening schedules. By contrast, the same study cited by ASTQB says untrained amateur personnel, including developers themselves, seldom top 35% for any form of testing. The study also notes that ordinary development personnel can introduce new bugs while fixing older ones, with bad-fix injection rates topping 7%.
That is a major difference, and it becomes even more important in an AI-assisted development workflow. When code is being suggested by a model, stitched together from prompts, or drafted quickly under deadline pressure, the need for disciplined independent testing gets stronger. Certified testers are trained to think in terms of risk, coverage, failure modes, and defect detection. They are not just looking for whether the code appears reasonable. They are trained to find what is wrong with it before a customer does. Post-production defects are very expensive, making the ROI on a software testing certification better than 1500% if the tester prevents even just one bug from making it to that stage.
Developers and testers are not interchangeable
Developers and testers work toward the same goal, but they do not bring the same perspective. Developers are primarily focused on building and changing software. Testers are trained to evaluate it from a different angle, challenge assumptions, prioritize risks, and design meaningful coverage. That difference has always mattered, but AI makes it harder to pretend that development alone is enough. If developers themselves do not fully trust AI-generated code, and fewer than half always verify it before commit, then relying on developers alone to absorb all AI verification work is not a very strong quality strategy.
Certified testers help close that gap because verification is their whole point of existence, not an extra task squeezed into a busy sprint. They bring structured test design, independence, and a professional discipline built around exposing defects. ASTQB also emphasizes risk-based testing as a core benefit of certification, which matters because modern teams rarely have enough time to test everything equally. The ability to focus effort where failure would do the most damage becomes even more valuable when AI increases code volume and shortens delivery cycles.
The rise of AI coding should raise the profile of testing
There is a lazy narrative that better AI code generation will eventually make testing less necessary. Current evidence does not support that. It supports the opposite. AI is making it easier to produce code, but not easier to trust it, as the survey shows. More code generation means more need for review. More plausible-looking output means more need for skilled validation from certified QA experts. More speed means more pressure on quality controls.
The organizations that handle this well will be the ones that treat certified software testers as a core part of AI-era delivery, not as a cleanup function at the end, aka shift-left. They will recognize that AI can help draft code, explain code, and accelerate development, but it cannot supply independent judgment, careful test design, or the trained instinct to find subtle defects. Those remain human strengths, and they are exactly the strengths that certified testers bring. Even as AI learns how to test better, it can still have the same blind spots as it did when it wrote the code.
What smart teams should do next
The practical takeaway seems pretty clear, even if AI advocates don’t want to admit it. Teams adopting AI coding tools should not reduce their emphasis on testing, but should strengthen it. They should put formal verification closer to the beginning of their process, make room for independent test thinking, and invest in certified testers who know how to challenge code that only looks correct on the surface. Yes, AI can increase development speed, but certified software testers are what help make sure that speed turns into dependable software rather than faster defect escape.
FAQ: Certified Software Testers and AI Coding
Why are certified software testers more important now that AI is writing more code?
Because AI increases code output faster than it increases trust in that output. Sonar’s 2026 State of Code survey found that developers report meaningful productivity gains from AI, but 96% still do not fully trust that AI-generated code is functionally correct. That makes independent verification more valuable, not less.
If AI helps developers code faster, why is more testing needed?
Faster code creation does not prove correctness. Sonar found that 61% of developers agree AI often produces code that looks correct but is not reliable, and only 48% say they always check AI-assisted code before committing it. That means AI can speed up development while also increasing the amount of code that still needs careful review and testing.
Do developers actually trust AI-generated code?
Not fully. Sonar’s official 2026 survey reports that 96% of developers do not fully trust that AI-generated code is functionally correct. The same research describes this as a verification gap: teams are using AI heavily, but confidence in the output has not kept pace.
Why can’t developers just test their own AI-assisted code?
Developers absolutely play a key role in quality, but ASTQB cites a study that untrained amateur testing, including testing done only by developers, performs far worse than formal testing by certified personnel. That study also showed that certified personnel can help drive defect removal efficiency above 99%, while untrained personnel, such as developers themselves, seldom top 35% for any form of testing.
What do certified software testers bring that others often miss?
Certified testers are trained to approach software with a different mindset. They focus on risk, coverage, edge cases, failure modes, and defect detection rather than assuming the code is fine because it looks clean or works on the happy path. ASTQB’s data shows that formal testing by certified personnel materially improves defect removal and reduces business risk.
Does AI reduce the need for software testing?
The evidence points the other way. AI helps produce more code, but Sonar’s data shows that developers still spend significant effort reviewing, testing, and correcting AI output. As AI-generated code becomes more common, the need for people who know how to validate that code becomes more central to software quality.
How does certification help a business, not just the tester?
Certification is a business advantage because better defect removal lowers the chance of costly escaped defects, production issues, and rework. Studies show that formal testing by certified personnel can improve quality while also lowering costs and shortening schedules.
What should companies do if they are adopting AI coding tools?
They should treat testing as a stronger control point, not a weaker one. Sonar’s findings suggest the main challenge is no longer just generating code, but reliably reviewing and validating it. That makes it sensible for companies to invest in certified testers and formal verification practices as AI coding use expands.