# Writing Bug Reports for Software Repair Agents: What Information Matters Most? arXiv: https://arxiv.org/abs/2607.09553 ยท HTML: https://mlsanity.com/paper/2607.09553/ ## 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 29 of its 69 references that are in the map. - First posted on arXiv 10 Jul 2026. ## Placed in - LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/) ## Builds on (most cited first, 29 of 29) - [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022) - [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023) - [SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering](https://mlsanity.com/paper/2405.15793/) (2024) - [OpenHands: An Open Platform for AI Software Developers as Generalist Agents](https://mlsanity.com/paper/2407.16741/) (2024) - [SWE-smith: Scaling Data for Software Engineering Agents](https://mlsanity.com/paper/2504.21798/) (2025) - [AutoCodeRover: Autonomous Program Improvement](https://mlsanity.com/paper/2404.05427/) (2024) - [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) - [Live-SWE-agent: Can Software Engineering Agents Self-Evolve on the Fly?](https://mlsanity.com/paper/2511.13646/) (2025) - [Trae Agent: An LLM-based Agent for Software Engineering with Test-time Scaling](https://mlsanity.com/paper/2507.23370/) (2025) - [CodeR: Issue Resolving with Multi-Agent and Task Graphs](https://mlsanity.com/paper/2406.01304/) (2024) - [SpecRover: Code Intent Extraction via LLMs](https://mlsanity.com/paper/2408.02232/) (2024) - [SWE-Exp: Experience-Driven Software Issue Resolution](https://mlsanity.com/paper/2507.23361/) (2025) - [Alibaba LingmaAgent: Improving Automated Issue Resolution via Comprehensive Repository Exploration](https://mlsanity.com/paper/2406.01422/) (2024) - [A Survey of Learning-based Automated Program Repair](https://mlsanity.com/paper/2301.03270/) (2023) - [MASAI: Modular Architecture for Software-engineering AI Agents](https://mlsanity.com/paper/2406.11638/) (2024) - [Prompting Is All You Need: Automated Android Bug Replay with Large Language Models](https://mlsanity.com/paper/2306.01987/) (2023) - [A Systematic Literature Review on Large Language Models for Automated Program Repair](https://mlsanity.com/paper/2405.01466/) (2024) - [EXPEREPAIR: Dual-Memory Enhanced LLM-based Repository-Level Program Repair](https://mlsanity.com/paper/2506.10484/) (2025) - [DARS: Dynamic Action Re-Sampling to Enhance Coding Agent Performance by Adaptive Tree Traversal](https://mlsanity.com/paper/2503.14269/) (2025) - [Inside the Scaffold: A Source-Code Taxonomy of Coding Agent Architectures](https://mlsanity.com/paper/2604.03515/) (2026) - [Assessing the Quality of the Steps to Reproduce in Bug Reports](https://mlsanity.com/paper/1906.07107/) (2019) - [FlexFL: Flexible and Effective Fault Localization with Open-Source Large Language Models](https://mlsanity.com/paper/2411.10714/) (2024) - [Auto-completing Bug Reports for Android Applications](https://mlsanity.com/paper/1705.04016/) (2017) - [BURT: A Chatbot for Interactive Bug Reporting](https://mlsanity.com/paper/2302.06050/) (2023) - [Combining Language and App UI Analysis for the Automated Assessment of Bug Reproduction Steps](https://mlsanity.com/paper/2502.04251/) (2025) - [Automated Table Reproduction via Code Generation](https://mlsanity.com/paper/2602.10046/) (2026) - [Yet Even Less Is Even Better For Agentic, Reasoning, and Coding LLMs](https://mlsanity.com/paper/2604.00824/) (2026) - [ImproBR: Bug Report Improver Using LLMs](https://mlsanity.com/paper/2604.26142/) (2026) ## Built on by (most cited first, 1 of 1) - [Can LLMs Fix It Without Code? Toward Automated Verification of No-Code Bug Fixes](https://mlsanity.com/paper/2610.11963/) (2026) ## Abstract (arXiv) Software development is increasingly moving toward agentic-first workflows. This includes AI agents responsible for generating initial fixes for submitted issue reports. In this setting, issue reports are no longer merely documentation for human maintainers; they become the primary task specification for the agent. However, little is known about how such reports should be written to maximize the agent's chances of producing a correct fix. We study what makes a bug report agent-ready. Starting from the SWE-bench Verified benchmark (i.e., a collection of 500 real repository issues with human-written gold patches and test suites for evaluating generated fixes) we manually classify each issue by change type (e.g., bug fix vs refactoring) and annotate each sentence with its information type, such as observed behavior, expected behavior, reproduction steps, localization cues, and suggested fixes. We focus on the 441 issues representing bug reports, and we run on them mini-swe-agent using three LLM backbones (i.e., GPT-5-mini, MiniMax M2.5, and Gemini 3 Flash). We then fit a binomial regression model to estimate the incremental association between each information type and agent success, controlling for confounding factors. Our results suggest that agentic-first reports benefit most from information that narrows the agent's search and repair space. Localization cues, such as references to affected code areas, are positively associated with successful repairs, while suggested fixes, expressed either in code or natural language, show some of the strongest positive associations with pass probability. An ablation study removing selected information types confirms that agents benefit less from information traditionally useful to humans, such as reproduction steps, and more from sentences that expose a repair direction, either through bug localization or a suggested fix.