5 papers
Benchmarking LLMs for Fine-Grained Code Review with Enriched Context in Practice
Ruida Hu, Xinchen Wang, Xin-Cheng Wen +5
Code review is a cornerstone of software quality assurance, and recent advances in Large Language Models (LLMs) have shown promise in its automation. However, existing benchmarks f…
CodeRepoQA: A Large-scale Benchmark for Software Engineering Question Answering
Ruida Hu, Chao Peng, Jingyi Ren +6
In this work, we introduce CodeRepoQA, a large-scale benchmark specifically designed for evaluating repository-level question-answering capabilities in the field of software engine…
DialogAgent: An Auto-engagement Agent for Code Question Answering Data Production
Xiaoyun Liang, Jingyi Ren, Jiayi Qi +2
Large Language Models (LLMs) have become increasingly integral to enhancing developer productivity, particularly in code generation, comprehension, and repair tasks. However, fine-…
ContextModule: Improving Code Completion via Repository-level Contextual Information
Zhanming Guan, Junlin Liu, Jierui Liu +7
Large Language Models (LLMs) have demonstrated impressive capabilities in code completion tasks, where they assist developers by predicting and generating new code in real-time. Ho…
A Real-World Benchmark for Evaluating Fine-Grained Issue Solving Capabilities of Large Language Models
Ruida Hu, Chao Peng, Jingyi Ren +6
Automatically resolving software issues is crucial for software development in practice, impacting the software quality and user experience. The process of resolving real-world iss…