数据来源：HuggingFace Papers

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1. SWE-at-Scale: Scalable Issue Synthesis from Historical Bug Fixes

Abstract:As coding agents become increasingly capable, robust evaluation requires a broader and more diverse supply of verifiable software engineering (SWE) tasks. However, real-world tasks are constrained by the natural frequency of issue reporting and the substantial cost of manual curation. Furthermore, existing synthetic approaches rely primarily on the pretrained knowledge of large models coupled with heuristic prompting, often failing to capture the authentic, complex mechanics of software failures. To exponentially scale SWE task production, we introduce SWE-at-Scale, a framework that automatically synthesizes realistic issues by reverse-engineering real-world software fixes. Our key insight is that historical fixes encapsulate deep mechanistic knowledge of bug formation: inverting a fix transforms repair supervision into bug-induction supervision grounded in actual development scenarios. SWE-at-Scale transfers these learned bug patterns to novel code contexts, repeatedly sampling diverse bug typologies to construct executable SWE tasks. Extensive experiments show that SWE-at-Scale generates more diverse and realistic SWE tasks than current synthesis baselines while posing greater challenges to state-of-the-art coding agents. Notably, SWE-at-Scale can generate distinct bug types from a single code context, with bug diversity scaling robustly even up to 200 generations per file. Overall, SWE-at-Scale turns finite real-world fixes into reusable supervision for scalable synthesis of realistic, diverse, and challenging SWE tasks.

中文摘要

摘要：随着编码代理能力的不断提升，可靠的评估需要更广泛和多样化的可验证软件工程（SWE）任务。然而，现实世界的任务受到问题报告自然频率和人工整理的高成本限制。此外，现有的合成方法主要依赖大型模型的预训练知识结合启发式提示，往往无法捕捉软件故障的真实复杂机制。为了指数级地扩展SWE任务的生成，我们提出了SWE-at-Scale框架，该框架通过逆向工程现实世界的软件修复来自动合成真实问题。我们的关键见解是，历史修复封装了漏洞形成的深层机制知识：将修复逆转，可以将修复监督转化为基于实际开发场景的缺陷诱导监督。SWE-at-Scale将这些学习到的漏洞模式迁移到新的代码上下文中，反复采样多样的漏洞类型，以构建可执行的SWE任务。大量实验表明，SWE-at-Scale生成的SWE任务比现有合成基线更加多样和真实，同时对最先进的编码代理提出了更高挑战。值得注意的是，SWE-at-Scale可以从单一代码上下文生成不同类型的漏洞，且漏洞多样性在每个文件多达200次生成时仍能稳健扩展。总体而言，SWE-at-Scale将有限的现实修复转化为可重复使用的监督，用于可扩展地合成真实、多样且具有挑战性的SWE任务。

LLM Analysis

LLM Analysis Failed: Error: Kimi内容未就绪或抓取失败: 2610.21359

Authors: Silin Chen,Zihan Zhang,Xiaodong Gu,Jiale Huang,Yuling Shi,Han Li,Zhao Shan,Chengcheng Wan,Zhensu Sun,Haibing Guan

Categories:

PDF URL: https://arxiv.org/pdf/2610.21359.pdf

Arxiv URL: https://arxiv.org/abs/2610.21359

Arxiv ID: 2610.21359

CoolPaper URL: https://papers.cool/arxiv/2610.21359

Published: 2026-10-16T01:39:08.985Z

Updated: 2026-10-16T01:39:08.985Z