activity
20232025
most citedWhen LLMs Meet API Documentation: Can Retrieval Augmentation Aid Code Generation Just as It Helps Developers?

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

cs.SE2025

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…

cs.SE2025

What Builds Effective In-Context Examples 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), which incorporates code examples inside t…

cs.SE20251 cited

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…

cs.SE2024

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…

cs.SE2023

SURE: A Visualized Failure Indexing Approach using Program Memory Spectrum

Yi Song, Xihao Zhang, Xiaoyuan Xie +3

Failure indexing is a longstanding crux in software testing and debugging, the goal of which is to automatically divide failures (e.g., failed test cases) into distinct groups acco…