8 citations · 10 across the 14 of their papers we have counts for
8 papers · 1 filter
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…