MASAI: Modular Architecture for Software-engineering AI Agents

Daman Arora; Atharv Sonwane; Nalin Wadhwa; Abhav Mehrotra; Saiteja Utpala; Ramakrishna Bairi; Aditya Kanade; Nagarajan Natarajan

arXiv · 2406.11638

MASAI: Modular Architecture for Software-engineering AI Agents

Daman Arora·Atharv Sonwane·Nalin Wadhwa·Abhav Mehrotra·Saiteja Utpala·Ramakrishna Bairi·Aditya Kanade·Nagarajan Natarajan

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Abstract

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.

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Daman Arora, Atharv Sonwane, Nalin Wadhwa, Abhav Mehrotra, Saiteja Utpala, Ramakrishna Bairi, Aditya Kanade, Nagarajan Natarajan. 2024-06-17. MASAI: Modular Architecture for Software-engineering AI Agents. https://arxiv.org/abs/2406.11638

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