Why reviewing AI-generated code is devilishly hard
Reviewing AI-generated code presents unique challenges due to the lack of objective understanding required from developers. Cognitive biases, such as the Dunning-Kruger effect, can lead to overconfidence in one's ability to evaluate AI-generated changes. This situation is exacerbated by the plausibility of AI outputs, which can mask underlying faults and reduce independent verification efforts.
- ▪When using AI assistance, developers may mistakenly believe they understand code changes that they do not.
- ▪Metacognitive skills are crucial for assessing one's understanding of AI-generated code.
- ▪Junior programmers are particularly at risk of accepting faulty AI-generated code due to cognitive biases.
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Here’s the thing: when working on code with GenAI assistance (from a chat-bot, through IDE auto-completion, or, increasingly, with an AI agent) you need a better understanding of the system than when working without. Cognitive psychology and the workings of large language models (LLMs) give us four clues on why this happens. When working without AI assistance on a non-trivial task and on code you don’t know, you first need to comprehend it in order to perform your task. Otherwise you’re hacking (in the sense of performing undisciplined changes), not programming, and most likely you won’t go anywhere (fast). This is an objective built-in control gate of the human-only software development process: if you don’t understand the code, you can’t contribute to it and you you fail.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Spinellis.
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