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.
66f4fd21c0d90ca46e369cf7
Huy Nhat Phan
66f4fd21c0d90ca46e369cf8
Phong X. Nguyen
66f4fd21c0d90ca46e369cf9
Nghi D. Q. Bui
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.
HyperAgent, a multi-agent system, achieves state-of-the-art performance across diverse software engineering tasks using four specialized agents, potentially revolutionizing AI-assisted development.
Large Language Models software engineering autonomous software agents multi-agent system Planner Navigator Code Editor Executor SWE-Bench-Lite SWE-Bench-Verified GitHub issue resolution RepoExec repository-level code generation fault localization program repair Defects4J