Title: Advancing Code Intelligence with Language Models
Abstract: Large language models have broadly revolutionized programming and software development. In this talk, Yangruibo “Robin” Ding will discuss his research on enabling LLMs to meet the real-world demands of software engineering. First, he will describe how we can improve LLMs’ code reasoning capabilities by training them with comprehensive program semantics, enhancing their effectiveness in code generation, runtime analysis, and self-debugging. Second, Ding will discuss how we can adapt LLMs for realistic programming practice, enabling these models to retrieve additional context, interact with symbolic tools to collect feedback, and iteratively refine their solutions. Third, he will introduce his efforts to develop code-embedding language models that represent program functionalities with vectors to support non-generative tasks, such as code search, clone retrieval, and vulnerability detection. Finally, Ding 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 PhD 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 for code. Ding 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 capabilities 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. His work has won an ACM SIGSOFT Distinguished Paper Award and was an IEEE Transactions on Software Engineering Best Paper Runner-Up; he has also received an IBM PhD Fellowship Award.