title: HyperAgent: Generalist Software Engineering Agents to Solve Coding Tasks
    at Scale
summary: 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.
paper_authors._id: 66f4fd21c0d90ca46e369cf7
paper_authors.name: Huy Nhat Phan
paper_authors._id: 66f4fd21c0d90ca46e369cf8
paper_authors.name: Phong X. Nguyen
paper_authors._id: 66f4fd21c0d90ca46e369cf9
paper_authors.name: Nghi D. Q. Bui
paper_title: HyperAgent: Generalist Software Engineering Agents to Solve Coding Tasks
    at Scale
paper_summary: 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.
paper_ai_keywords: LLMs (Language Models) software engineering (SE) autonomous software agents multi-agent system generalist multi-agent system coding tasks Planner Navigator Code Editor Executor SE tasks development tasks programming languages full lifecycle conception verification SWE-Bench-Lite SWE-Bench-Verified GitHub issue resolution repository-level code generation (RepoExec) fault localization program repair Defects4J autonomous agents complex, multi-step SE tasks domains and languages