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.
Alexandra Mendes
Assistant Professor
My research focuses on encouraging a wider adoption of software verification by creating tools and methods that hide the complexities of verifying software. Much of my most recent work overlaps with the area of software engineering. For more details, see selected publications and some of my projects. Follow me on Twitter or add me on LinkedIn. See also the Software Reliability Lab website.