119 citations · 253 across the 10 of their papers we have counts for
6 papers · 1 filter
When AI Takes the Wheel: Security Analysis of Framework-Constrained Program Generation
Yue Liu, Zhenchang Xing, Shidong Pan +1
In recent years, the AI wave has grown rapidly in software development. Even novice developers can now design and generate complex framework-constrained software systems based on t…
Pitfalls in Language Models for Code Intelligence: A Taxonomy and Survey
Xinyu She, Yue Liu, Yanjie Zhao +5
Modern language models (LMs) have been successfully employed in source code generation and understanding, leading to a significant increase in research focused on learning-based co…
Large Language Models for Software Engineering: A Systematic Literature Review
Xinyi Hou, Yanjie Zhao, Yue Liu +7
Large Language Models (LLMs) have significantly impacted numerous domains, including Software Engineering (SE). Many recent publications have explored LLMs applied to various SE ta…
Refining ChatGPT-Generated Code: Characterizing and Mitigating Code Quality Issues
Yue Liu, Thanh Le-Cong, Ratnadira Widyasari +4
We systematically study the quality of 4,066 ChatGPT-generated code implemented in two popular programming languages, i.e., Java and Python, for 2,033 programming tasks. The goal o…
On the Reliability and Explainability of Language Models for Program Generation
Yue Liu, Chakkrit Tantithamthavorn, Yonghui Liu +1
Recent studies have adopted pre-trained language models, such as CodeT5 and CodeGPT, for automated program generation tasks like code generation, repair, and translation. Numerous…
Automatically Recommend Code Updates: Are We There Yet?
Yue Liu, Chakkrit Tantithamthavorn, Yonghui Liu +2
In recent years, large pre-trained Language Models of Code (CodeLMs) have shown promising results on various software engineering tasks. One such task is automatic code update reco…