InfraFix: Technology-Agnostic Repair of Infrastructure as Code

2025-03-21

AI Summary

Automated repair of Infrastructure-as-Code (IaC) scripts lacks a robust, technology-agnostic framework capable of generalizing across diverse IaC languages and error types. Method: We propose the first generic, multi-source–guided IaC repair framework, integrating system-call monitoring, state inference, and SMT-based constraint solving to construct large-scale, high-fidelity repair scenarios. Our approach jointly leverages syntactic, semantic, and runtime-state constraints to guide patch generation. Contribution/Results: Evaluated on 254,755 real-world IaC repair scenarios, our framework achieves a 95.5% repair success rate—substantially improving coverage, generalizability, and practical applicability over prior work. It establishes a foundational methodology for automated program repair (APR) in the IaC domain, enabling cross-language, context-aware correction without requiring language-specific heuristics or manual intervention.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Large language models for search
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

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 254,755 repair scenarios with a success rate of 95.5%. 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.

Problem

• Repairing Infrastructure as Code scripts without technology constraints
• Addressing lack of specifications for automated IaC error correction
• Enabling large-scale evaluation of IaC repair techniques

Innovation

• Technology-agnostic framework for IaC repair
• SMT-based repair and state inference modules
• Large-scale repair scenario generation method