DevDiff Platform Uses ML and LLMs to Flag Risky Pull Requests in Real Time

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A developer has built DevDiff, a pull request risk intelligence platform designed to address shortcomings in traditional static code analysis tools, such as high false-positive rates and lack of context. The platform combines a 20-rule security engine, a Random Forest machine learning model with an F1 score of approximately 0.92, and optional LLM-powered deep review via Groq to assess and score code changes. DevDiff integrates with GitHub via OAuth, supports multiple repositories, and streams findings to a dashboard in real time using WebSockets. It also offers a CLI tool for local pre-review scanning and provides analytics including developer risk scorecards, security heatmaps, and historical risk trends. A notable feature allows the platform to learn from developer feedback on false positives, gradually adjusting detection thresholds over time.

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