For software development and quality assurance engineers, generative and agentic AI have created something of a paradox. On the one hand, teams can now write code faster than ever. On the other, they’re struggling more than ever to validate the quality and reliability of that code.
Organizations aiming to capitalize fully on AI as a way of accelerating software delivery must find ways to resolve this paradox. Specifically, they need to ensure that software validation keeps pace with software development.
That’s not easy, but it’s possible for teams that rethink their software delivery tooling and methodologies.
Why software quality is falling behind software production speed
The reason why teams are increasingly struggling to validate the quality of their software is simple enough: In the era of modern AI, engineers can write code much faster than they can test it.
After all, AI has transformed coding, which was once the slowest part of the software delivery process, into the shortest part. AI models can spit out hundreds of lines of code per minute, whereas even seasoned programmers might take days to do the same.
Validating code, however, requires running tests using the same types of tools and techniques that engineers employed in the pre-AI era. Most of those tests can, and should, be automated. Test automation means that engineers write code to define what to test for, then execute the tests automatically.
Yet, even with help from test automation, the software validation process still takes a lot of time. Actually executing tests doesn’t always take very long. But other key parts of the validation workflow, such as interpreting test results and fixing flaky tests (ones that don’t always reliably produce the same outcomes), can be a manual, time-consuming endeavor. Even prior to AI-assisted development, test automation sometimes struggled to keep pace with software development as a result. In the AI era, software teams are pushing features by the minute, and traditional test automation processes simply can’t keep up.
AI has made software testing more important than ever
The challenge of keeping software testing processes in sync with AI-assisted coding is all the more acute given that it’s becoming increasingly clear that AI-generated code requires more extensive testing. CodeRabbit found that AI-generated code contains 1.7 times more problems than code written the old-fashioned way. In a similar vein, 46% of developers say they “actively distrust” AI coding tools, compared to 33% who trust them.
These figures don’t mean that no one should use AI to code; on the contrary, the vast efficiency boosts that AI-assisted coding offers outweigh the higher likelihood of bugs.
They do mean, however, that rigorous validation of AI-generated code via automated testing has become especially critical. Every organization seeking to benefit from AI in the software delivery process while also releasing software with confidence needs to test its software comprehensively. Otherwise, it risks deploying applications that contain major performance, reliability, or security bugs.
Speeding up software validation
But again, the fact remains that engineers assisted by AI coding tools generate code much faster than traditional test automation methodologies can validate it, leaving teams struggling to test code as quickly as it flows down the software delivery pipeline.
Fortunately, it doesn’t have to be this way. It’s possible to speed up the validation process by taking advantage of the same AI capabilities that have become central to software production in recent years.
Specifically, development and QA teams can take advantage of AI to assist with tasks such as:
- Writing test scripts, as well as updating them whenever applications or testing requirements change.
- Optimizing test execution to help ensure that tests complete as quickly as possible.
- Interpreting test results to determine when re-tests are necessary, as well as which deficiencies to prioritize.
AI can’t handle these tasks entirely on their own; just as experienced developers are necessary to help fix flaws in AI-generated code, reliable software validation requires skilled QA professionals who know when AI is leading them astray, and who can make strategic decisions involving context like business priorities that AI doesn’t understand.
Still, when integrated into software delivery pipelines via test orchestration platforms that incorporate AI capabilities, AI-assisted validation plays a key role in bridging the velocity gap between software development and software quality assurance.
By extension, more modern approaches to software testing make it possible to speed up the overall software delivery process without compromising on quality or security. That’s the ultimate goal because writing code faster with help from AI is not very beneficial if the rest of the software delivery process remains slow, or if release velocity comes at the price of quality. By extending AI into the validation process, development and QA teams can finally have it all.
