Trustworthy AI-Assisted Programming: Detection and Repair of Unreliable Code

Abstract

The widespread adoption of AI-assisted coding tools has fundamentally transformed software development, enabling rapid code generation but simultaneously introducing new risks to software reliability. This thesis addresses the critical challenge of ensuring trustworthy AI-assisted programming through two complementary approaches: detecting AI-generated code and advancing automated program repair. I present four major contributions: (1) The first comprehensive empirical study of AI-generated code detection across 2.24 million samples with fine-tuning-based improvements, (2) Defects4C, the first large-scale executable C/C++ bug benchmark with 248 real-world bugs, (3) Novel automated repair methods combining deep learning and LLM-based approaches with extensive empirical evaluation, and (4) A semantic enhancement framework that incorporates execution traces to improve LLM reasoning for program repair. These contributions establish new foundations for trustworthy, semantically grounded automated program repair in the era of AI-assisted development.