MASAI: Modular Architecture for Software-engineering AI Agents
<|reference_start|>MASAI: Modular Architecture for Software-engineering AI Agents: A common method to solve complex problems in software engineering, is to divide the problem into multiple sub-problems. Inspired by this, we propose a Modular Architecture for Software-engineering AI (MASAI) agents, where different LLM-powered sub-agents are instantiated with well-defined objectives and strategies tuned to achieve those objectives. Our modular architecture offers several advantages: (1) employing and tuning different problem-solving strategies across sub-agents, (2) enabling sub-agents to gather information from different sources scattered throughout a repository, and (3) avoiding unnecessarily long trajectories which inflate costs and add extraneous context. MASAI enabled us to achieve the highest performance (28.33% resolution rate) on the popular and highly challenging SWE-bench Lite dataset consisting of 300 GitHub issues from 11 Python repositories. We conduct a comprehensive evaluation of MASAI relative to other agentic methods and analyze the effects of our design decisions and their contribution to the success of MASAI.<|reference_end|>
@article{arora2024masai:,
    title={MASAI: Modular Architecture for Software-engineering AI Agents},
    author={Daman Arora, Atharv Sonwane, Nalin Wadhwa, Abhav Mehrotra, Saiteja
  Utpala, Ramakrishna Bairi, Aditya Kanade, Nagarajan Natarajan},
    journal={arXiv preprint arXiv:2406.11638},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.11638},
    primaryClass={cs.AI cs.SE}
}