AI is creating more software flaws – and they're getting worse

AI is helping developers write code faster, but it's also leading to more problems, marking the latest study to highlight adoption concerns among software developers.

In a study from CodeRabbit, researchers found AI makes 1.7 more times as many mistakes as human programmers.

To come to that conclusion, CodeRabbit looked at 470 open source GitHub pull requests, of which 320 had AI input and 150 were human only.

"The results? Clear, measurable, and consistent with what many developers have been feeling intuitively: AI accelerates output, but it also amplifies certain categories of mistakes," wrote CodeRabbit's director of AI David Loker in a blog post.

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He admitted the study wasn't perfect, in part because it was difficult to double-check authorship, but said the findings matched with previous research. Loker pointed to a report by Cortex that found pull requests per author increased by 20%, but incidents per pull request also went up — by 23.%.

That matches with previous research suggesting AI-generated code is now the cause of one-in-five breaches, with another report noting coders' concerns around AI introducing errors to their work.

More problems and more critical flaws

The CodeRabbit study found 10.83 issues with AI pull requests versus 6.45 for human-only ones, adding that AI pull requests were far more likely to have critical or major issues.

"Even more striking: high-issue outliers were much more common in AI PRs, creating heavy review workloads," Loker said.

Logic and correctness was the worst area for AI code, followed by code quality and maintainability and security. Because of that, CodeRabbit advised reviewers to watch out for those types of errors in AI code.

"These include business logic mistakes, incorrect dependencies, flawed control flow, and misconfigurations," Loker wrote. "Logic errors are among the most expensive to fix and most likely to cause downstream incidents."

AI code was also spotted omitting null checks, guardrails, and other error checking, which Loker noted are issues that can lead to outages in the real world.

When it came to security, the most common mistake by AI was improper password handling and insecure object references, Loker noted, with security issues 2.74 times more common in AI code than that written by humans.

Another major difference between AI code and human written-code was readability. "AI-produced code often looks consistent but violates local patterns around naming, clarity, and structure," Loker added.

Beyond those issues, Loker noted flaws in concurrency, formatting, and naming inconsistencies — all of which could not only cause issues with software or apps, but also make it harder for human reviewers to spot problems.

What's happening, and how to fix it

Some of the faults are down to a lack of context, Loker suggested, and in other cases AI is sticking to "generic defaults" rather than company rules, such as with naming patterns or architectural norms.

Security problems may be down to how models recreate "legacy patterns or outdated practices" by using older code as training data.

"AI generates surface-level correctness: It produces code that looks right but may skip control-flow protections or misuse dependency ordering," Loker added.

None of this means AI shouldn't be used in coding, he stressed. Instead, companies should ensure AI has context of business rules, policies or style guides, and be given guidelines for basic security.

Such efforts would wipe out many of the faults spotted, he said. Similarly, code reviewers should be given a checklist that takes this research into account, so they know what types of errors to watch out for.

"The future of AI-assisted development isn’t about replacing developers," he added. "It’s about building systems, workflows, and safety layers that amplify what AI does well while compensating for what it tends to miss."

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