AI and Code Review: When the Reviewer Itself Isn't Reviewed
·2 min read·Intermediate
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Artificial Intelligence was supposed to simplify our lives, but sometimes it complicates things in unexpected ways. Take code review, the quality control of software: now AI is sticking its nose in.
In 30 seconds
01AI assists developers with code review, but there's a fundamental issue at play.
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What this means for you
For the average person, this means the software we use might be less reliable if developers over-rely on unverified AI. It's a reminder that technology is merely a tool, and it requires thoughtful application.
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·2 min·2·Beginner
The AI itself, acting as a "reviewer," hasn't been properly tested for its role.
03This risks lowering code quality and fostering a false sense of security in development.
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Has AI become your chief code reviewer?
It seems so, at least judging by how many developers now use Artificial Intelligence for code review. The idea is brilliant: AI speeds up the process, catches trivial errors, and suggests improvements, effectively turning every developer into a more efficient "reviewer."
Many believe AI is an infallible helper, almost a superhero for code. But here's a curious detail: who actually tested this AI's reviewing capabilities? It's like handing someone a hammer without checking if they know how to use it properly.
Heinrich Neb, a developer, raised this exact point in a recent dev.to article, after reading Michael Amachree's perspective. Amachree argued AI made him a worse reviewer; Neb countered that the real problem lies with the untried AI itself.
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Why is an untested reviewer a problem?
An untested reviewer, even an artificial one, can create more problems than it solves, especially in the delicate world of software development. The risk is that AI, while flagging some errors, might miss others or even introduce new ones with faulty suggestions.
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Think about code quality. If the AI hasn't been trained adequately or lacks the "eyes" for nuances, we might end up with software riddled with hidden bugs. This means companies should compare AI suggestions against those of experienced human reviewers.
The issue is both psychological and technical: developers blindly trust AI, delegating the most critical part of quality control. This leads to a reduction in human reviewing skills and, ultimately, professional regression. Who reviews the reviewers?
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How can we truly trust AI (or can we)?
To trust AI with critical tasks like code review, it's essential to treat it like any other tool or team member: it must be tested, validated, and continuously monitored. Simply turning it on and hoping for the best isn't enough.
Companies should invest in creating specific benchmarks to evaluate AI's performance as a reviewer. This means comparing its suggestions against those of experienced human reviewers, using real and complex codebases.
In essence, before allowing AI to "promote" code, we should "promote" the AI itself, ensuring it truly deserves our trust. Only then can we avoid mistaking speed for quality, ending up with more headaches than solutions.
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