Introduction
Modern software teams merge hundreds of Pull Requests every day, but traditional static analysis tools often generate excessive false positives, lack contextual understanding, and provide little insight into the overall risk of a code change.
To address this challenge, I built DevDiff—a Real-Time Pull Request Risk Intelligence Platform that combines rule-based security analysis, machine learning, and optional Large Language Model (LLM) reasoning to identify risky code changes before they reach production.
Rather than simply listing vulnerabilities, DevDiff helps developers understand how risky a Pull Request is, why it is risky, and how those risks evolve over time.
The Problem
