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MASAI: Modular Architecture for Software-engineering AI Agents
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MASAI: Modular Architecture for Software-engineering AI Agents
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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.
Forward citations
Cited by 14 Pith papers
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Inside the Scaffold: A Source-Code Taxonomy of Coding Agent Architectures
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Investigating Test Overfitting on SWE-bench
The first empirical study of test overfitting shows that auto-generated tests from issues can lead to code that passes observed tests but misses important cases or breaks functionality in SWE-bench issue resolution.
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IssueExec: A Test-Driven Approach for Localizing Software Engineering Issues
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Multi-Agent LLM Collaboration for Unit Test Generation via Human-Testing-Inspired Workflows
A three-agent LLM workflow plus a test-specialized knowledge graph generates unit tests that beat prior LLM and search-based tools on coverage and mutation score.
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From Historical Patches to Repair Plans: Outcome-Conditioned Reasoning for Repository-Level Program Repair
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Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory
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Agentless: Demystifying LLM-based Software Engineering Agents
Agentless, a basic three-phase LLM pipeline for bug localization, repair, and validation, outperforms complex open-source agents on SWE-bench Lite with 32% success rate at $0.70 cost.
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Towards Enabling An Artificial Self-Construction Software Life-cycle via Autopoietic Architectures
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S-AI-Recursive operationalizes reasoning as a closed-loop hormonal iteration with Clarifine and Confusionin to reach stable equilibrium, achieving competitive benchmark performance with under 10 million parameters via...
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