31 citations · 52 across the 13 of their papers we have counts for
5 papers · 1 filter
SWE-Cycle: Benchmarking Code Agents across the Complete Issue Resolution Cycle
Hao Guan, Lingyue Fu, Shao Zhang +8
As autonomous code agents move toward end-to-end software development, evaluating their practical autonomy becomes critical. Current benchmarks hide friction by testing agents in p…
DebugTA: An LLM-Based Agent for Simplifying Debugging and Teaching in Programming Education
Lingyue Fu, Haowei Yuan, Datong Chen +5
In programming education, Debugging and Teaching (DT) task is a common scenario where students receive assistance in correcting their erroneous code. The task involves multiple inp…
Automatically Benchmarking LLM Code Agents through Agent-Driven Annotation and Evaluation
Lingyue Fu, Bolun Zhang, Hao Guan +7
Recent advances in code agents have enabled automated software development at the project level, supported by large language models (LLMs). However, existing benchmarks for code ag…
CoreCodeBench: Decoupling Code Intelligence via Fine-Grained Repository-Level Tasks
Lingyue Fu, Hao Guan, Bolun Zhang +10
The evaluation of Large Language Models (LLMs) for software engineering has shifted towards complex, repository-level tasks. However, existing benchmarks predominantly rely on coar…
CodeGRAG: Bridging the Gap between Natural Language and Programming Language via Graphical Retrieval Augmented Generation
Kounianhua Du, Jizheng Chen, Renting Rui +7
Utilizing large language models to generate codes has shown promising meaning in software development revolution. Despite the intelligence shown by the large language models, their…