From Code Generation Towards Software Engineering: Advancing Code Intelligence w/ Language Models
Yangruibo (Robin) Ding
IRB 4105 or https://umd.zoom.us/j/94340703410?pwd=rrXaGSXSpabcMTtDNmeCNf2Ih2fQYE.1
Tuesday, April 1, 2025, 11:00 am-12:00 pm 
Abstract
Large language models (LLMs) have revolutionized the way how humans write code, but they still have limitations in comprehensively reasoning about software programs to assist with more involved software engineering tasks. In this talk, I will first provide an overview of my research on enhancing LLMs' code intelligence, optimizing each step of the development pipeline towards more complex software engineering tasks. I will then delve into my key contributions, focusing on how to equip LLMs with (1) symbolic reasoning for program semantics and (2) global reasoning for software dependencies. Finally, I will discuss the future of AI-driven software engineering, envisioning a path to approach full-stack automation in a trustworthy 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.
This talk is organized by Samuel Malede Zewdu