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New open-source platform enables controlled evaluation of AI code refactoring agents

By PulseAugur Editorial · [1 sources] ·

Researchers have developed RefactorPlatform, an open-source tool designed to evaluate the performance of AI agents in large-scale code refactoring tasks. This platform standardizes the evaluation environment, allowing for controlled comparisons of different design choices, including model backbones like GitHub Copilot CLI and OpenRouter, various execution strategies, and prompt variations. Initial tests on 100 multi-file tasks demonstrated that AST-aware chunking improves accuracy by 25-30% over naive token-window chunking, and a single retrieval-augmented agent outperformed a multi-agent delegation approach. AI

IMPACT Enables reproducible evaluation of AI code refactoring agents, potentially accelerating development and improving their reliability.

RANK_REASON The cluster describes a new open-source research platform and its initial evaluation results, published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New open-source platform enables controlled evaluation of AI code refactoring agents

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The cluster describes a new open-source research platform and its initial evaluation results, published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Aziz Ben Amor, Drish Mali, Mann Acharya, Vijayasri Iyer, S\'ebastien Brati\`eres ·

    RefactorPlatform: An Open-Source Harness for Controlled Evaluation of Repository-Scale Refactoring Agents

    arXiv:2609.04898v1 Announce Type: cross Abstract: Repository-scale refactoring requires coding agents to propagate a single change across many interdependent files without altering program behavior, yet to our knowledge no existing harness isolates the design choices that determi…

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