8 papers
Securing Code Understanding: Detecting Natural Backdoor Vulnerability in Code Language Models
Yuchen Chen, Weisong Sun, Haocheng Huang +11
Code Language Models (CodeLMs) have become integral to software engineering, significantly advancing code intelligence tasks. However, their widespread adoption has raised critical…
Probing Privacy Leaks in LLM-based Code Generation via Test Generation
Yifei Ge, Zhenpeng Chen, Weisong Sun +7
The widespread availability of large-scale code datasets has fueled the rapid development of large language models (LLMs) for code-related tasks. These datasets may include sensiti…
Promptware Engineering: Software Engineering for Prompt-Enabled Systems
Zhenpeng Chen, Chong Wang, Weisong Sun +3
Large Language Models (LLMs) are increasingly integrated into software applications, giving rise to a broad class of prompt-enabled systems, in which prompts serve as the primary '…
REprompt: Prompt Generation for Intelligent Software Development Guided by Requirements Engineering
Junjie Shi, Weisong Sun, Zhenpeng Chen +4
The rapid development of large language models is transforming software development. Beyond serving as code auto-completion tools in integrated development environments, large lang…
A Study on Thinking Patterns of Large Reasoning Models in Code Generation
Kevin Halim, Sin G. Teo, Ruitao Feng +4
Currently, many large language models (LLMs) are utilized for software engineering tasks such as code generation. The emergence of more advanced models known as large reasoning mod…
Security of Language Models for Code: A Systematic Literature Review
Yuchen Chen, Weisong Sun, Chunrong Fang +7
Language models for code (CodeLMs) have emerged as powerful tools for code-related tasks, outperforming traditional methods and standard machine learning approaches. However, these…