Towards Effective Validation and Integration of LLM-Generated Code

Ningzhi Tang

Abstract: Recent advances in large language model (LLM)-based code generation tools have shown the potential to lower the barrier to programming and improve developer productivity. However, validating the generated codes and integrating them into projects present significant challenges, especially for developers with limited expertise or domain-specific knowledge. I present a novel approach to help developers understand and modify LLM-generated code to align with their intentions, informed by my empirical study of developer behaviors when working with LLM code generation models. In the future, I propose to extend the work to include support for project-level code validation, dynamic program behavior integration, developer behavioral and cognitive context modeling, and computer science education.