HyperAgent: Generalist Software Engineering Agents to Solve Coding Tasks at Scale
<|reference_start|>HyperAgent: Generalist Software Engineering Agents to Solve Coding Tasks at Scale: Large Language Models (LLMs) have revolutionized software engineering (SE), demonstrating remarkable capabilities in various coding tasks. While recent efforts have produced autonomous software agents based on LLMs for end-to-end development tasks, these systems are typically designed for specific SE tasks. We introduce HyperAgent, a novel generalist multi-agent system designed to address a wide spectrum of SE tasks across different programming languages by mimicking human developers' workflows. Comprising four specialized agents - Planner, Navigator, Code Editor, and Executor. HyperAgent manages the full lifecycle of SE tasks, from initial conception to final verification. Through extensive evaluations, HyperAgent achieves state-of-the-art performance across diverse SE tasks: it attains a 25.01% success rate on SWE-Bench-Lite and 31.40% on SWE-Bench-Verified for GitHub issue resolution, surpassing existing methods. Furthermore, HyperAgent demonstrates SOTA performance in repository-level code generation (RepoExec), and in fault localization and program repair (Defects4J), often outperforming specialized systems. This work represents a significant advancement towards versatile, autonomous agents capable of handling complex, multi-step SE tasks across various domains and languages, potentially transforming AI-assisted software development practices.<|reference_end|>
@article{phan2024hyperagent:,
    title={HyperAgent: Generalist Software Engineering Agents to Solve Coding Tasks
  at Scale},
    author={Huy Nhat Phan, Tien N. Nguyen, Phong X. Nguyen, Nghi D. Q. Bui},
    journal={arXiv preprint arXiv:2409.16299},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.16299},
    primaryClass={cs.SE cs.AI}
}