3 citations · 3 across the 6 of their papers we have counts for
7 papers
ExplainBench: Evaluating Code Explanations from Agents
Zhiyuan Pan, Sungmin Kang, Imam Nur Bani Yusuf +1
Large Language Model (LLM) agents have seen rapid adoption in software engineering. As agents take a greater role in the actual generation of code, they are making larger changes,…
Code Review Agent Benchmark
Yuntong Zhang, Zhiyuan Pan, Imam Nur Bani Yusuf +3
Software engineering agents have shown significant promise in writing code. As AI agents permeate code writing, and generate huge volumes of code automatically -- the matter of cod…
Open the Oyster: Empirical Evaluation and Improvement of Code Reasoning Confidence in LLMs
Shufan Wang, Xing Hu, Junkai Chen +2
With the widespread application of large language models (LLMs) in the field of code intelligence, increasing attention has been paid to the reliability and controllability of thei…
Re-Evaluating Code LLM Benchmarks Under Semantic Mutation
Zhiyuan Pan, Xing Hu, Xin Xia +1
In the era of large language models (LLMs), code benchmarks have become an important research area in software engineering and are widely used by practitioners. These benchmarks ev…
CATCODER: Repository-Level Code Generation with Relevant Code and Type Context
Zhiyuan Pan, Xing Hu, Xin Xia +1
Large language models (LLMs) have demonstrated remarkable capabilities in code generation tasks. However, repository-level code generation presents unique challenges, particularly…
Reasoning Runtime Behavior of a Program with LLM: How Far Are We?
Junkai Chen, Zhiyuan Pan, Xing Hu +3
Large language models for code (i.e., code LLMs) have shown strong code understanding and generation capabilities. To evaluate the capabilities of code LLMs in various aspects, man…