一句话：首个技术无关的 IaC 自动修复框架，支持多源信息引导修复，并生成大规模修复场景进行评估。
基础设施即代码（IaC）支持可扩展的自动化 IT 基础设施管理，但脚本错误可能引发安全漏洞、服务中断与数据丢失。已有研究多聚焦于 IaC 问题检测，自动程序修复（APR）因缺乏合适规格而研究不足。本文提出 InfraFix，首个技术无关的 IaC 脚本修复框架，允许 APR 技术由多种信息源引导；同时提出一种修复场景生成方法，以支持对 IaC 修复技术的大规模评估。作者以基于 SMT 的修复模块和基于系统调用的状态推断模块实现并评估 InfraFix，在 254,288 个修复场景上达到 95.7% 的成功率。该工作为推进 IaC 领域的 APR 研究提供了基础，使研究者可借助 InfraFix 试验新的状态推断与修复技术，并用所提场景生成方法进行规模化评估。
Infrastructure as Code (IaC) enables scalable and automated IT infrastructure management but is prone to errors that can lead to security vulnerabilities, outages, and data loss. While prior research has focused on detecting IaC issues, Automated Program Repair (APR) remains underexplored, largely due to the lack of suitable specifications. In this work, we propose InfraFix, the first technology-agnostic framework for repairing IaC scripts. Unlike prior approaches, InfraFix allows APR techniques to be guided by diverse information sources. Additionally, we introduce a novel approach for generating repair scenarios, enabling large-scale evaluation of APR techniques for IaC. We implement and evaluate InfraFix using an SMT-based repair module and a state inference module that uses system calls, demonstrating its effectiveness across 254,288 repair scenarios with a success rate of 95.7%. Our work provides a foundation for advancing APR in IaC by enabling researchers to experiment with new state inference and repair techniques using InfraFix and to evaluate their approaches at scale with our repair scenario generation method.