HyperAgent: Generalist Software Engineering Agents to Solve Coding Tasks at Scale
원문 초록
Large Language Models (LLMs) have revolutionized software engineering (SE), showcasing remarkable proficiency in various coding tasks. Despite recent advancements that have enabled the creation of autonomous software agents utilizing LLMs for end-to-end development tasks, these systems are typically designed for specific SE functions. We introduce HyperAgent, an innovative generalist multi-agent system designed to tackle a wide range of SE tasks across different programming languages by mimicking the workflows of human developers. HyperAgent features four specialized agents-Planner, Navigator, Code Editor, and Executor-capable of handling the entire lifecycle of SE tasks, from initial planning to final verification. HyperAgent sets new benchmarks in diverse SE tasks, including GitHub issue resolution on the renowned SWE-Bench benchmark, outperforming robust baselines. Furthermore, HyperAgent demonstrates exceptional performance in repository-level code generation (RepoExec) and fault localization and program repair (Defects4J), often surpassing state-of-the-art baselines.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
An Empirical Study on Strong-Weak Model Collaboration for Repo-level Code Generation
CORE: Benchmarking LLMs Code Reasoning Capabilities through Static Analysis Tasks
How Does LLM Reasoning Work for Code? A Survey and a Call to Action
An LLM-as-Judge Metric for Bridging the Gap with Human Evaluation in SE Tasks
How Accurately Do Large Language Models Understand Code?
The Devil behind the mask: An emergent safety vulnerability of Diffusion LLMs
토론
이 논문과 연결된 포럼 글이 아직 없습니다.