Have linters or other code-analysis tools become capable of distinguishing AI-generated code from code written by a person? I realize that some implementations may naturally look identical, but are there recurring patterns, design choices, or other clues that could reliably separate AI-produced code from human-written code? If detection is possible today, how dependable is it likely to be?
3 Answers
AI-generated code can sometimes favor familiar, easy-to-explain patterns because those patterns appear frequently in its training material. For example, it may repeatedly suggest a singleton or produce boilerplate-heavy solutions. That can be a useful hint during review, but humans use the same patterns too, so it cannot serve as a dependable identification method.
A linter isn’t designed to identify who wrote the code. It checks things like syntax, formatting, type errors, and common bugs. You could build a separate analysis tool that looks for statistical or stylistic patterns, but any reliable detector would probably be easy to evade by feeding its results back into the generation process and asking for more human-like output.
In practice, it’s extremely difficult to distinguish AI-written code from human-written code based only on the final source. Unless the code includes some kind of watermark or there’s a record of how it was produced, there usually isn’t definitive proof. Patterns such as overly generic comments, repetitive structure, or common design choices might be clues, but they aren’t reliable evidence.
That matches what I was wondering about—recurring habits might give clues, but they don’t seem strong enough to prove authorship.