Our Framework for Reviewing AI-Generated Code - The JetBrains Blog

Research by JetBrains’ Human-AI eXperience team and Lund University argues that reviewing AI-generated code is primarily a trust-calibration challenge rather than a conventional diff-reading task, because language models can produce large, multi-file changes while offering no reliable indication of which portions are uncertain or risky. Based on participatory workshops with 17 practitioners and a survey of 43 software professionals, the proposed framework recommends a three-level workflow in which reviewers first gain a high-level overview, then assess risk at the file level, and finally inspect selected code snippets in detail. This approach aims to help developers allocate attention according to segment-level risk instead of auditing every generated line, while restoring contextual signals that are normally available when reviewing a human colleague’s work. Existing tools such as CodeRabbit, Claude Code, Graphite, and GitHub’s AI review features offer partial support through summaries, severity tags, or smaller review units, but the researchers identify a gap in tools that explicitly guide reviewers from overview to risk stratification to detailed analysis.

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