26 citations · 55 across the 25 of their papers we have counts for
19 papers · 1 filter
Refine After Generation: Toward Correct and Concise Patches in LLM-based Program Repair
Wenqiang Luo, Jacky Keung, Xiaoyu Shi +4
Large language models (LLMs) have advanced automatic program repair (APR) to the point where agentic systems routinely resolve real-world, repository-level issues. Yet the generate…
How Reasoning Shapes Social Bias in LLM-Generated Code?
Weifeng Sun, Jieke Shi, Zhou Yang +4
Large language models (LLMs) are increasingly used for code generation, yet generated programs may exhibit social bias through unfair or differential treatment of sensitive demogra…
Fail-Fast, Restart-Smart: Early Failure Prediction and Restart for SWE Agentic Tasks
Chenyu Wang, Yunbo Lyu, Junda He +4
Software engineering (SWE) agents resolve repository-level issues through long trajectories that grow increasingly expensive as context accumulates. Failed runs tend to be longer a…
How Do Practitioners Build SE Agents? Insights from a Mixed-Methods Study
Yunbo Lyu, David Williams, Jieke Shi +5
The rise of Software Engineering (SE) agents, i.e., LLM-based agents that can understand large codebases and carry out engineering tasks with limited human intervention, has been m…
Bridging Behavior and Implementation: Automated Java Glue Code Generation for Behavior-Driven Development
Xinyu Shi, Zhou Yang, An Ran Chen
Behavior-Driven Development (BDD) helps technical and non-technical stakeholders share a common understanding of software requirements through natural-language scenarios. Glue code…
Hidden Licensing Risks in the LLMware Ecosystem
Bo Wang, Yueyang Chen, Jieke Shi +5
Large Language Models (LLMs) are increasingly integrated into software systems, giving rise to a new class of systems referred to as LLMware. Beyond traditional source-code compone…
Token Sugar: Making Source Code Sweeter for LLMs through Token-Efficient Shorthand
Zhensu Sun, Chengran Yang, Xiaoning Du +3
Large language models (LLMs) have shown exceptional performance in code generation and understanding tasks, yet their high computational costs hinder broader adoption. One importan…
Scrub It Out! Erasing Sensitive Memorization in Code Language Models via Machine Unlearning
Zhaoyang Chu, Yao Wan, Zhikun Zhang +7
While Code Language Models (CLMs) have demonstrated superior performance in software engineering tasks such as code generation and summarization, recent empirical studies reveal a…
"My productivity is boosted, but ..." Demystifying Users' Perception on AI Coding Assistants
Yunbo Lyu, Zhou Yang, Jieke Shi +3
This paper aims to explore fundamental questions in the era when AI coding assistants like GitHub Copilot are widely adopted: what do developers truly value and criticize in AI cod…
SLICEMATE: Accurate and Scalable Static Program Slicing via LLM-Powered Agents
Jianming Chang, Jieke Shi, Yunbo Lyu +5
Static program slicing, which extracts the executable portions of a program that affect the values at a specific location, supports many software analysis tasks such as debugging a…
Mut4All: Fuzzing Compilers via LLM-Synthesized Mutators Learned from Bug Reports
Bo Wang, Pengyang Wang, Chong Chen +9
Mutation-based fuzzing is effective for uncovering compiler bugs, but designing high-quality mutators for modern languages with complex constructs (e.g., templates, macros) remains…
Synthesizing Efficient and Permissive Programmatic Runtime Shields for Neural Policies
Jieke Shi, Junda He, Zhou Yang +2
With the increasing use of neural policies in control systems, ensuring their safety and reliability has become a critical software engineering task. One prevalent approach to ensu…
Hotfixing Large Language Models for Code
Zhou Yang, David Lo
Large Language Models for Code (LLM4Code) have become an integral part of developers' workflows, assisting with tasks such as code completion and generation. However, these models…
Ecosystem of Large Language Models for Code
Zhou Yang, Jieke Shi, Premkumar Devanbu +1
The availability of vast amounts of publicly accessible data of source code and the advances in modern language models, coupled with increasing computational resources, have led to…
AI Coders Are Among Us: Rethinking Programming Language Grammar Towards Efficient Code Generation
Zhensu Sun, Xiaoning Du, Zhou Yang +2
Artificial Intelligence (AI) models have emerged as another important audience for programming languages alongside humans and machines, as we enter the era of large language models…
Efficient and Green Large Language Models for Software Engineering: Literature Review, Vision, and the Road Ahead
Jieke Shi, Zhou Yang, David Lo
Large Language Models (LLMs) have recently shown remarkable capabilities in various software engineering tasks, spurring the rapid growth of the Large Language Models for Software…
Robustness, Security, Privacy, Explainability, Efficiency, and Usability of Large Language Models for Code
Zhou Yang, Zhensu Sun, Terry Zhuo Yue +2
Large language models for code (LLM4Code), which demonstrate strong performance (e.g., high accuracy) in processing source code, have significantly transformed software engineering…
Industry Practitioners Perspectives on AI Model Quality: Perceptions, Challenges, and Solutions
Chenyu Wang, Zhou Yang, Yunbo Lyu +3
Artificial Intelligence (AI) is now used across nearly every industry, making AI model quality essential for building reliable and trustworthy systems. Historically, correctness ha…
Assessing AI Detectors in Identifying AI-Generated Code: Implications for Education
Wei Hung Pan, Ming Jie Chok, Jonathan Leong Shan Wong +6
Educators are increasingly concerned about the usage of Large Language Models (LLMs) such as ChatGPT in programming education, particularly regarding the potential exploitation of…