12 citations · 21 across the 10 of their papers we have counts for
10 papers
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…
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…
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…
Exploring Data Management Challenges and Solutions in Agile Software Development: A Literature Review and Practitioner Survey
Ahmed Fawzy, Amjed Tahir, Matthias Galster +1
Context: Managing data related to a software product and its development poses significant challenges for software projects and agile development teams. These include integrating d…
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…
Copilot-in-the-Loop: Fixing Code Smells in Copilot-Generated Python Code using Copilot
Beiqi Zhang, Peng Liang, Qiong Feng +2
As one of the most popular dynamic languages, Python experiences a decrease in readability and maintainability when code smells are present. Recent advancements in Large Language M…
An Exploratory Study on Automatic Identification of Assumptions in the Development of Deep Learning Frameworks
Chen Yang, Peng Liang, Zinan Ma
Stakeholders constantly make assumptions in the development of deep learning (DL) frameworks. These assumptions are related to various types of software artifacts (e.g., requiremen…
Security Weaknesses of Copilot-Generated Code in GitHub Projects: An Empirical Study
Yujia Fu, Peng Liang, Amjed Tahir +4
Modern code generation tools utilizing AI models like Large Language Models (LLMs) have gained increased popularity due to their ability to produce functional code. However, their…