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

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.