8 citations · 10 across the 14 of their papers we have counts for
14 papers
Understanding Agent-Reactive Bugs at the Model-Harness Boundary: An Empirical Study of LLM Agent Issue Reports
Jingyi Chen, Songqiang Chen, Hengcheng Zhu +3
LLM agents span command-line interfaces (e.g., Codex) and agent frameworks (e.g., LangChain), integrating backend LLMs with harness code that parses model outputs, controls agent l…
Inside the Skill Market: From Software Engineering Activities to Reusable Agent Skills
Jialun Cao, Xinru Yan, Songqiang Chen +3
Software engineering (abbrev. SE) has continuously evolved through increasingly powerful forms of reuse, from source code and libraries to components and services. Recent advances…
MR-Coupler: Automated Metamorphic Test Generation via Functional Coupling Analysis
Congying Xu, Hengcheng Zhu, Songqiang Chen +3
Metamorphic testing (MT) is a widely recognized technique for alleviating the oracle problem in software testing. However, its adoption is hindered by the difficulty of constructin…
CAM: A Causality-based Analysis Framework for Multi-Agent Code Generation Systems
Zongyi Lyu, Zhenlan Ji, Songqiang Chen +4
Despite the remarkable success that Multi-Agent Code Generation Systems (MACGS) have achieved, the inherent complexity of multi-agent architectures produces substantial volumes of…
Multi-Agent Systems for Dataset Adaptation in Software Engineering: Capabilities, Limitations, and Future Directions
Jingyi Chen, Xiaoyan Guo, Songqiang Chen +2
Automating the adaptation of software engineering (SE) research artifacts across datasets is essential for scalability and reproducibility, yet it remains largely unstudied. Recent…
LSPFuzz: Hunting Bugs in Language Servers
Hengcheng Zhu, Songqiang Chen, Valerio Terragni +4
The Language Server Protocol (LSP) has revolutionized the integration of code intelligence in modern software development. There are approximately 300 LSP server implementations fo…
Understanding and Bridging the Planner-Coder Gap: A Systematic Study on the Robustness of Multi-Agent Systems for Code Generation
Zongyi Lyu, Songqiang Chen, Zhenlan Ji +5
Multi-agent systems (MASs) have emerged as a promising paradigm for automated code generation, demonstrating impressive performance on established benchmarks. Despite their prosper…
Can Emulating Semantic Translation Help LLMs with Code Translation? A Study Based on Pseudocode
Songqiang Chen, Congying Xu, Jingyi Chen +3
Although large language models (LLMs) show promising potential in code translation, they still struggle to generate accurate translations using the commonly adopted direct code-to-…
RulER: Automated Rule-Based Semantic Error Localization and Repair for Code Translation
Shuo Jin, Songqiang Chen, Xiaoyuan Xie +1
Automated code translation aims to convert programs between different programming languages while maintaining their functionality. Due to the imperfections of code translation mode…
What Makes In-Context Examples Effective for Code Generation?
Dongze Li, Songqiang Chen, Jialun Cao +1
In-Context Learning (ICL) has emerged as a promising solution to enhance the code generation capabilities of Large Language Models (LLMs) by incorporating code examples inside the…
When LLMs Meet API Documentation: Can Retrieval Augmentation Aid Code Generation Just as It Helps Developers?
Jingyi Chen, Songqiang Chen, Jialun Cao +2
Retrieval-augmented generation (RAG) has increasingly shown its power in extending large language models' (LLMs') capability beyond their pre-trained knowledge. Existing works have…
Isolating Language-Coding from Problem-Solving: Benchmarking LLMs with PseudoEval
Jiarong Wu, Songqiang Chen, Jialun Cao +2
Existing code generation benchmarks for Large Language Models (LLMs) such as HumanEval and MBPP are designed to study LLMs' end-to-end performance, where the benchmarks feed a prob…
CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit
Jialun Cao, Songqiang Chen, Wuqi Zhang +2
Data contamination presents a critical barrier preventing widespread industrial adoption of advanced software engineering techniques that leverage code language models (CLMs). This…
MR-Adopt: Automatic Deduction of Input Transformation Function for Metamorphic Testing
Congying Xu, Songqiang Chen, Jiarong Wu +4
While a recent study reveals that many developer-written test cases can encode a reusable Metamorphic Relation (MR), over 70% of them directly hard-code the source input and follow…