HyperAgent: Generalist Software Engineering Agents to Solve Coding Tasks at Scale

Phan Nhật Huy, Tien N Nguyen, Nghi D. Q. Bui

Keywords: Multi-Agent, Large Language Model, Software Engineering

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 26.00% success rate on SWE-Bench-Lite and 33.00% on SWE-Bench-Verified for GitHub issue resolution, surpassing existing methods. Furthermore, HyperAgent demonstrates superior performance in code generation at repository scale (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.

Primary Area: other topics in machine learning (i.e., none of the above)

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Submission Number: 5583