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.

我们提出了一种名为 MASAI 的软件工程人工智能模块化架构，该架构利用分解问题为多个子问题的方法，通过实例化具有明确定义的目标和策略的 LLM 驱动的子代理来解决复杂问题，并在高难度的 SWE-bench Lite 数据集上实现了最高性能（28.33% 的解决率）。