1 citations · 1 across the 3 of their papers we have counts for
6 papers · 1 filter
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…
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 '…
Show Me Your Code! Kill Code Poisoning: A Lightweight Method Based on Code Naturalness
Weisong Sun, Yuchen Chen, Mengzhe Yuan +6
Neural code models (NCMs) have demonstrated extraordinary capabilities in code intelligence tasks. Meanwhile, the security of NCMs and NCMs-based systems has garnered increasing at…
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…
Towards Trustworthy LLMs for Code: A Data-Centric Synergistic Auditing Framework
Chong Wang, Zhenpeng Chen, Tianlin Li +2
LLM-powered coding and development assistants have become prevalent to programmers' workflows. However, concerns about the trustworthiness of LLMs for code persist despite their wi…