AI Code Is a Bug-Filled Mess

AI tools have repeatedly been found to be unreliable and inaccurate, which can lead to mistakes falling through the cracks and even forcing some programmers to put in long hours to identify and correct them.

Adding to the reality check, a new report by AI software company CodeRabbit found that code generated by an AI was far more error-prone than the human-written stuff — and by a significant margin. Across the 470 pull requests the company analyzed, AI code produced an average 10.83 issues per request, while human-authored code produced just 6.45.

In other words, AI code produced 1.7 times more issues than human code, once again highlighting major weaknesses plaguing generative AI tools.

CodeRabbit found that AI-generated code produced a higher rate of “critical” and “major” issues, in a “meaningful rise in substantive concerns that demand reviewer attention.”

AI code was also most likely to contain errors related to logic and correctness. However, the biggest weakness CodeRabbit found was in code quality and readability, which are issues that can “slow teams down and compound into long-term technical debt.”

Then there are serious cybersecurity concerns, with generated code introducing issues related to improper password handling that could lead to protected information being exposed, among other insecure practices.

Security firm Apiiro also found in its research that developers who used AI produce ten times more security problems than their counterparts who don’t use the tech.