AI-assisted coding is becoming a common tool in software development, but new research suggests it still has some issues to work out. A recent report by CodeRabbit compared AI-generated code with human-written code and found that AI code tends to introduce more problems. Developers are using these tools to speed up their work, but the trade-offs are becoming clearer.

More Issues in AI-Generated Code

The report analyzed 470 open source pull requests on GitHub, including 320 created with AI assistance and 150 likely written by humans. It found that AI co-authored pull requests had an average of 10.83 issues, compared to 6.45 issues in human-only code. This means AI-generated code generally has more problems to fix before it can be used safely.

Interestingly, issues in AI pull requests weren’t just more frequent—they also showed a wider spread. This means some PRs had many problems, making reviews more time-consuming. Developers reported that AI pull requests often require deeper scrutiny, as their issues are more variable and sometimes more complex to catch.

Types of Problems and Risks

The biggest problems with AI code were related to logic and correctness. But AI-generated code also had more issues in areas like security, maintainability, and performance. While none of these issues were unique to AI, their higher occurrence increases the risk of bugs slipping through. The report warns teams that security vulnerabilities are more common in AI-assisted code, which could pose serious threats if not carefully checked.

On the bright side, the report found some unexpected benefits. Human-written code had nearly twice as many spelling mistakes, possibly because humans include more comments and inline explanations. Also, human code had more testability issues, which might reflect differences in coding style or practice. Overall, AI can help catch some errors, but it also introduces new challenges that teams need to manage carefully.

Best Practices and Future Considerations

The report emphasizes that as AI tools become more integrated into development workflows, developers should set clear boundaries. Providing project-specific context—like architectural rules and constraints—can help AI produce better code. Using strict continuous integration (CI) rules for formatting, naming, and readability can also reduce some of the issues associated with AI-generated code.

The findings suggest that teams need to be cautious. AI can boost productivity but also amplifies certain mistakes. Developers should expect more variability in AI-assisted pull requests and allocate extra time for reviews. Proper safeguards and guidelines are essential to make AI a helpful tool rather than a source of new problems.

As AI continues to evolve, it’s clear that it offers both opportunities and challenges. With careful implementation and oversight, it can be a powerful addition to the developer’s toolkit—but not without its drawbacks. Ongoing research and best practices will be key to harnessing AI’s full potential while keeping code quality high.

Inspired by

• https://www.infoworld.com/article/4109129/ai-assisted-coding-creates-more-problems-report.html

Sources

• coderabbit.ai
• coderabbit.ai