Code Review

Code diffs and pull requests are analyzed by AI agents to provide inline review comments and bug detection.

Business impact

• Pull Request (PR) Review Time — AI reduces review time by automating detection of bugs and style issues
• Code Quality — Automated reviews catch logical errors and security risks improving overall quality
• Developer Productivity — Developers spend less time on low-level reviews and more on complex tasks
• Remediation Cycle Time — Faster feedback loops enable quicker fixes, cutting remediation from days to hours

Data requirements

• Source code diffs and pull request metadata (Text) — Used to analyze code changes and context for review comments
• Code repositories and version control history (Structured) — Provides historical context and patterns for training AI models
• Static analysis and security scan results (Structured) — Augments AI with known vulnerability and style violation data
• Developer feedback and review comments (Text) — Used to improve AI suggestions and reduce false positives

AI methods and techniques

• Predictive AI — Predicts potential bugs and code issues based on learned patterns
• Agentic AI — Uses multi-agent systems to parallelize review and aggregate findings
• Generative AI — Generates inline comments, summaries, and suggested code fixes

AI models and model families

GPT-4o, Claude, CodeBERT, Google Gemini, Custom multi-agent AI systems

Industries

Real-world evidence

5 documented case studies on record.

Companies using this: Microsoft, Qodo, Sentry, Solarisbank, Unnamed.