8 papers
FasterPy: An LLM-based Code Execution Efficiency Optimization Framework
Yue Wu, Minghao Han, Ruiyin Li +5
Code often suffers from performance bugs. These bugs necessitate the research and practice of code optimization. Traditional rule-based methods rely on manually designing and maint…
An Insight into Security Code Review with LLMs: Capabilities, Obstacles, and Influential Factors
Jiaxin Yu, Peng Liang, Yujia Fu +4
Security code review is a time-consuming and labor-intensive process typically requiring integration with automated security defect detection tools. However, existing security anal…
On Fixing Insecure AI-Generated Code through Model Fine-Tuning and Prompting Strategies
Ali Soltanian Fard Jahromi, Amjed Tahir, Peng Liang +1
The security of AI-generated code remains a major obstacle to its widespread adoption. Although code generation models achieve strong performance on functional benchmarks, their ou…
Beyond Functional Correctness: Design Issues in AI IDE-Generated Large-Scale Projects
Syed Mohammad Kashif, Ruiyin Li, Peng Liang +4
New generation of AI coding tools, including AI-powered IDEs equipped with agentic capabilities, can generate code within the context of the project. These AI IDEs are increasingly…
A Survey of Bugs in AI-Generated Code
Ruofan Gao, Amjed Tahir, Peng Liang +2
Developers are widely using AI code-generation models, aiming to increase productivity and efficiency. However, there are also quality concerns regarding the AI-generated code. The…
On Developers' Self-Declaration of AI-Generated Code: An Analysis of Practices
Syed Mohammad Kashif, Peng Liang, Amjed Tahir
AI code generation tools have gained significant popularity among developers, who use them to assist in software development due to their capability to generate code. Existing stud…