AI-Driven Static Analysis & Unit Test Generation
This project involved the systematic categorization and labeling of source code to facilitate Al-driven static analysis for mobile applications. I curated and annotated a dataset of iOS codebases to identify logic patterns, fault localizations, and potential edge cases. This data was used to train models capable of automatically generating unit tests and identifying software vulnerabilities, bridging the gap between raw code and research-grade automated testing.