Dartmouth Events

Abstract: Large language models (LLMs) have broadly revolutionized programming and software development. In this talk, I will discuss my research on enabling LLMs to meet the real-world demands of software engineering. First, I will describe how we improve LLMs' code reasoning capabilities by training them with comprehensive program semantics, enhancing their effectiveness in code generation, runtime analysis, and self-debugging. Second, I will discuss how we adapt LLMs for the realistic programming practice, enabling these models to retrieve additional context, interact with symbolic tools to collect feedback, and iteratively refine their solutions. Third, I will introduce our efforts to develop code-embedding LMs that represent program functionalities with vectors to support non-generative tasks, such as code search, clone retrieval, and vulnerability detection. Finally, I will envision the future of AI systems for software engineering, which will achieve the next level of automation in a more reliable, intelligent, and cost-efficient way.

Bio: Yangruibo (Robin) Ding is a Ph.D. candidate in the Department of Computer Science at Columbia University. His research is at the intersection of Software Engineering and Machine Learning, focusing on developing large language models (LLMs) for code. He trains LLMs to generate, analyze, and refine software programs and constructs benchmarks to systematically evaluate LLMs in solving software engineering tasks. He also studies how to improve LLMs' reasoning capability to tackle complex programming tasks, such as debugging and patching. His interdisciplinary research has been published in top-tier conferences of software engineering, programming languages, natural language processing, and machine learning. He won an ACM SIGSOFT Distinguished Paper Award, an IEEE TSE Best Paper Runner-up, and received an IBM Ph.D. Fellowship.