Speaker:
Chunqiu Steven Xia
Talk Title:
Transforming Software Development in the Era of LLMs
Date and Location:
Monday, April 6, 2026
Bahen Centre for Information Technology, BA 3200
This lecture is open to the public. No registration is required, but space is limited.
The grad roundtable that follows the talk is open only to current University of Toronto Department of Computer Science graduate students.
Abstract:
AI and large language models (LLMs) are ushering in a new era of software development, fundamentally reshaping how systems are built, tested, and maintained. In this talk, I will present my research on integrating modern LLM capabilities with core software engineering principles to automate complex development tasks and strengthen the coding and reasoning abilities of AI systems. I will first introduce TitanFuzz, which leverages LLMs to implicitly solve complex and heterogeneous constraints for large-scale system testing, uncovering critical bugs in real-world software, and inspiring extensive subsequent work in LLM-driven testing. Next, I will discuss Agentless, which demonstrates that a simple, lightweight framework can effectively solve challenging repository-level software engineering tasks without heavy agentic overhead. I will showcase how Agentless has been adopted by leading AI companies to evaluate their latest models, inspire new agentic frameworks, and perform post- or mid-training for agentic coding and reasoning. In addition, I will highlight my other work on automating complex software tasks and my recent work to enable runtime self-improvement in AI coding systems. Together, my work establishes a scalable and practical foundation for building more capable and reliable AI-driven software systems.
About Chunqiu Steven Xia:
Chunqiu Steven Xia is a PhD candidate in Computer Science at UIUC, advised by Professor Lingming Zhang. His research lies at the intersection of Software Engineering and Artificial Intelligence, with a focus on automating critical and cognitively demanding software development tasks using large language models (LLMs) and software agents. His work has significantly advanced how fundamental software problems are addressed in both academia and industry, uncovering 200+ critical bugs and vulnerabilities in widely used systems, and being adopted by leading AI companies, including OpenAI, Meta, DeepSeek, and MiniMax. His research has been recognized with an ACM SIGSOFT Distinguished Paper Award (FSE 2025), an Oral Spotlight Paper Award (COLM 2024), a CACM Research Highlight (FSE 2023), and the prestigious Amazon AICE PhD Fellowship.