# Collaborative Agents for Automated Program Repair in Ruby arXiv: https://arxiv.org/abs/2511.03925 ยท HTML: https://mlsanity.com/paper/2511.03925/ ## Key facts - Cited by 1 papers in the map (data through 14 Oct 2026). - Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. - Builds on 39 of its 63 references that are in the map. - First posted on arXiv 6 Nov 2025. ## Placed in - LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/) ## Builds on (most cited first, 30 of 39) - [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023) - [DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence](https://mlsanity.com/paper/2401.14196/) (2024) - [OpenHands: An Open Platform for AI Software Developers as Generalist Agents](https://mlsanity.com/paper/2407.16741/) (2024) - [SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolution](https://mlsanity.com/paper/2502.18449/) (2025) - [Self-collaboration Code Generation via ChatGPT](https://mlsanity.com/paper/2304.07590/) (2023) - [MapCoder: Multi-Agent Code Generation for Competitive Problem Solving](https://mlsanity.com/paper/2405.11403/) (2024) - [SPoC: Search-based Pseudocode to Code](https://mlsanity.com/paper/1906.04908/) (2019) - [Structured Chain-of-Thought Prompting for Code Generation](https://mlsanity.com/paper/2305.06599/) (2023) - [MAGIS: LLM-Based Multi-Agent Framework for GitHub Issue Resolution](https://mlsanity.com/paper/2403.17927/) (2024) - [SWE-Search: Enhancing Software Agents with Monte Carlo Tree Search and Iterative Refinement](https://mlsanity.com/paper/2410.20285/) (2024) - [Keep the Conversation Going: Fixing 162 out of 337 bugs for 0.42 each using ChatGPT](https://mlsanity.com/paper/2304.00385/) (2023) - [InferFix: End-to-End Program Repair with LLMs](https://mlsanity.com/paper/2303.07263/) (2023) - [Learn-by-interact: A Data-Centric Framework for Self-Adaptive Agents in Realistic Environments](https://mlsanity.com/paper/2501.10893/) (2025) - [Is ChatGPT the Ultimate Programming Assistant -- How far is it?](https://mlsanity.com/paper/2304.11938/) (2023) - [Code Repair with LLMs gives an Exploration-Exploitation Tradeoff](https://mlsanity.com/paper/2405.17503/) (2024) - [RepairLLaMA: Efficient Representations and Fine-Tuned Adapters for Program Repair](https://mlsanity.com/paper/2312.15698/) (2023) - [xCodeEval: A Large Scale Multilingual Multitask Benchmark for Code Understanding, Generation, Translation and Retrieval](https://mlsanity.com/paper/2303.03004/) (2023) - [What Makes Good In-context Demonstrations for Code Intelligence Tasks with LLMs?](https://mlsanity.com/paper/2304.07575/) (2023) - [RAP-Gen: Retrieval-Augmented Patch Generation with CodeT5 for Automatic Program Repair](https://mlsanity.com/paper/2309.06057/) (2023) - [Preference Optimization for Reasoning with Pseudo Feedback](https://mlsanity.com/paper/2411.16345/) (2024) - [UniDebugger: Hierarchical Multi-Agent Framework for Unified Software Debugging](https://mlsanity.com/paper/2404.17153/) (2024) - [Enhancing repository-level software repair via repository-aware knowledge graphs](https://mlsanity.com/paper/2503.21710/) (2025) - [A Survey of LLM-based Automated Program Repair: Taxonomies, Design Paradigms, and Applications](https://mlsanity.com/paper/2506.23749/) (2025) - [Domain Knowledge Matters: Improving Prompts with Fix Templates for Repairing Python Type Errors](https://mlsanity.com/paper/2306.01394/) (2023) - [A Pair Programming Framework for Code Generation via Multi-Plan Exploration and Feedback-Driven Refinement](https://mlsanity.com/paper/2409.05001/) (2024) - [ContrastRepair: Enhancing Conversation-Based Automated Program Repair via Contrastive Test Case Pairs](https://mlsanity.com/paper/2403.01971/) (2024) - [APPATCH: Automated Adaptive Prompting Large Language Models for Real-World Software Vulnerability Patching](https://mlsanity.com/paper/2408.13597/) (2024) - [RePair: Automated Program Repair with Process-based Feedback](https://mlsanity.com/paper/2408.11296/) (2024) - [CREF: An LLM-based Conversational Software Repair Framework for Programming Tutors](https://mlsanity.com/paper/2406.13972/) (2024) - [When Large Language Models Confront Repository-Level Automatic Program Repair: How Well They Done?](https://mlsanity.com/paper/2403.00448/) (2024) ## Built on by (most cited first, 1 of 1) - [How Do LLMs Read Bug Reports? An Empirical Study of Attention in LLMs for Automated Program Repair](https://mlsanity.com/paper/2607.25873/) (2026) ## Abstract (arXiv) Automated Program Repair (APR) has advanced rapidly with Large Language Models (LLMs), but most existing methods remain computationally expensive, and focused on a small set of languages. Ruby, despite its widespread use in web development and the persistent challenges faced by its developers, has received little attention in APR research. In this paper, we introduce RAMP, a novel lightweight framework that formulates program repair as a feedback-driven, iterative process for Ruby. RAMP employs a team of collaborative agents that generate targeted tests, reflect on errors, and refine candidate fixes until a correct solution is found. Unlike prior approaches, RAMP is designed to avoid reliance on large multilingual repair databases or costly fine-tuning, instead operating directly on Ruby through lightweight prompting and test-driven feedback. Evaluation on the XCodeEval benchmark shows that RAMP achieves a pass@1 of 67% on Ruby, outper-forming prior approaches. RAMP converges quickly within five iterations, and ablation studies confirm that test generation and self-reflection are key drivers of its performance. Further analysis shows that RAMP is particularly effective at repairing wrong answers, compilation errors, and runtime errors. Our approach provides new insights into multi-agent repair strategies, and establishes a foundation for extending LLM-based debugging tools to under-studied languages.