27 citations · 42 across the 24 of their papers we have counts for
7 papers · 1 filter
Evaluating Software Process Models for Multi-Agent Class-Level Code Generation
Wasique Islam Shafin, Md Nakhla Rafi, Zhenhao Li +1
Modern software systems require code that is not only functional but also maintainable and well-structured. Although Large Language Models (LLMs) are increasingly used to automate…
Enhancing LLM-based Fault Localization with a Functionality-Aware Retrieval-Augmented Generation Framework
Xinyu Shi, Zhenhao Li, An Ran Chen
Fault localization (FL) is a critical but time-consuming task in software debugging, aiming to identify faulty code elements. While recent advances in large language models (LLMs)…
SecureVibeBench: Benchmarking Secure Vibe Coding of AI Agents via Reconstructing Vulnerability-Introducing Scenarios
Junkai Chen, Huihui Huang, Yunbo Lyu +10
Large language model-powered code agents are rapidly transforming software engineering, yet the security risks of their generated code have become a critical concern. Existing benc…
Defects4Log: Benchmarking LLMs for Logging Code Defect Detection and Reasoning
Xin Wang, Zhenhao Li, Zishuo Ding
Logging code is written by developers to capture system runtime behavior and plays a vital role in debugging, performance analysis, and system monitoring. However, defects in loggi…
RobuNFR: Evaluating the Robustness of Large Language Models on Non-Functional Requirements Aware Code Generation
Feng Lin, Dong Jae Kim, Zhenhao Li +3
When using LLMs to address Non-Functional Requirements (NFRs), developers may behave differently (e.g., expressing the same NFR in different words). Robust LLMs should output consi…
An Empirical Study of Retrieval-Augmented Code Generation: Challenges and Opportunities
Zezhou Yang, Sirong Chen, Cuiyun Gao +4
Code generation aims to automatically generate code snippets of specific programming language according to natural language descriptions. The continuous advancements in deep learni…
TrustRAG: Enhancing Robustness and Trustworthiness in Retrieval-Augmented Generation
Huichi Zhou, Kin-Hei Lee, Zhonghao Zhan +5
Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by integrating external knowledge sources, enabling more accurate and contextually relevant responses tai…