HyperAgent: Generalist Software Engineering Agents to Solve Coding Tasks
  at Scale
Authors: name: Huy Nhat Phan; name: Phong X. Nguyen; name: Nghi D. Q. Bui
Published: 2024-09-09 19:35:34

Abstract: 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.