Assessing the adoption of security policies by developers in terraform across different cloud providers.

IF 3.5 2区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING Empirical Software Engineering Pub Date : 2025-01-01 Epub Date: 2025-02-27 DOI:10.1007/s10664-024-10610-0
Alexandre Verdet, Mohammad Hamdaqa, Leuson Da Silva, Foutse Khomh
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引用次数: 0

Abstract

Cloud computing has become popular thanks to the widespread use of Infrastructure as Code (IaC) tools, allowing the community to manage and configure cloud infrastructure using scripts. However, the scripting process does not automatically prevent practitioners from introducing misconfigurations, vulnerabilities, or privacy risks. As a result, ensuring security relies on practitioners' understanding and the adoption of explicit policies. To understand how practitioners deal with this problem, we perform an empirical study analyzing the adoption of scripted security best practices present in Terraform files, applied on AWS, Azure, and Google Cloud. We assess the adoption of these practices by analyzing a sample of 812 open-source GitHub projects. We scan each project's configuration files, looking for policy implementation through static analysis (Checkov and Tfsec). The category Access policy emerges as the most widely adopted in all providers, while Encryption at rest presents the most neglected policies. Regarding the cloud providers, we observe that AWS and Azure present similar behavior regarding attended and neglected policies. Finally, we provide guidelines for cloud practitioners to limit infrastructure vulnerability and discuss further aspects associated with policies that have yet to be extensively embraced within the industry.

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来源期刊
Empirical Software Engineering
Empirical Software Engineering 工程技术-计算机:软件工程
CiteScore
8.50
自引率
12.20%
发文量
169
审稿时长
>12 weeks
期刊介绍: Empirical Software Engineering provides a forum for applied software engineering research with a strong empirical component, and a venue for publishing empirical results relevant to both researchers and practitioners. Empirical studies presented here usually involve the collection and analysis of data and experience that can be used to characterize, evaluate and reveal relationships between software development deliverables, practices, and technologies. Over time, it is expected that such empirical results will form a body of knowledge leading to widely accepted and well-formed theories. The journal also offers industrial experience reports detailing the application of software technologies - processes, methods, or tools - and their effectiveness in industrial settings. Empirical Software Engineering promotes the publication of industry-relevant research, to address the significant gap between research and practice.
期刊最新文献
The effect of data complexity on classifier performance. Reinforcement learning for online testing of autonomous driving systems: a replication and extension study. Understanding security tactics in microservice APIs using annotated software architecture decomposition models - a controlled experiment. Assessing the adoption of security policies by developers in terraform across different cloud providers. An empirical study on developers’ shared conversations with ChatGPT in GitHub pull requests and issues
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