About this session
This topic explores the integration of machine learning techniques into SemGrep for enhanced static analysis. It discusses the use of AI algorithms, natural language processing, and anomaly detection to improve rule creation, reduce false positives, and uncover complex code issues.
- Introduction to machine learning in static analysis with SemGrep
- Leveraging AI algorithms for rule creation and anomaly detection
- Enhancing rule precision and reducing false positives using ML techniques
- Case studies showcasing the effectiveness of ML-powered SemGrep analysis
- Challenges and future directions in AI-driven static analysis