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1. Security Smells in Ansible and Chef Scripts: A Replication Study

   https://doi.org/10.1145/3408897  

   Rahman, Akond; Rahman, Md Rayhanur; Parnin, Chris; Williams, Laurie (January 2021, ACM Transactions on Software Engineering and Methodology)

   null (Ed.)

   Context: Security smells are recurring coding patterns that are indicative of security weakness and require further inspection. As infrastructure as code (IaC) scripts, such as Ansible and Chef scripts, are used to provision cloud-based servers and systems at scale, security smells in IaC scripts could be used to enable malicious users to exploit vulnerabilities in the provisioned systems. Goal: The goal of this article is to help practitioners avoid insecure coding practices while developing infrastructure as code scripts through an empirical study of security smells in Ansible and Chef scripts. Methodology: We conduct a replication study where we apply qualitative analysis with 1,956 IaC scripts to identify security smells for IaC scripts written in two languages: Ansible and Chef. We construct a static analysis tool called Security Linter for Ansible and Chef scripts (SLAC) to automatically identify security smells in 50,323 scripts collected from 813 open source software repositories. We also submit bug reports for 1,000 randomly selected smell occurrences. Results: We identify two security smells not reported in prior work: missing default in case statement and no integrity check. By applying SLAC we identify 46,600 occurrences of security smells that include 7,849 hard-coded passwords. We observe agreement for 65 of the responded 94 bug reports, which suggests the relevance of security smells for Ansible and Chef scripts amongst practitioners. Conclusion: We observe security smells to be prevalent in Ansible and Chef scripts, similarly to that of the Puppet scripts. We recommend practitioners to rigorously inspect the presence of the identified security smells in Ansible and Chef scripts using (i) code review, and (ii) static analysis tools. 

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2. An empirical study of task infections in Ansible scripts

   https://doi.org/10.1007/s10664-023-10432-6  

   Rahman, Akond; Bose, Dibyendu Brinto; Zhang, Yue; Pandita, Rahul (January 2024, Empirical Software Engineering)

   Feldt, Robert; Zimmermann, Thomas (Ed.)

   Context Despite being beneficial for managing computing infrastructure at scale, Ansible scripts include security weaknesses, such as hard-coded passwords. Security weaknesses can propagate into tasks, i.e., code constructs used for managing computing infrastructure with Ansible. Propagation of security weaknesses into tasks makes the provisioned infrastructure susceptible to security attacks. A systematic characterization of task infection, i.e., the propagation of security weaknesses into tasks, can aid practitioners and researchers in understanding how security weaknesses propagate into tasks and derive insights for practitioners to develop Ansible scripts securely. Objective The goal of the paper is to help practitioners and researchers understand how Ansible-managed computing infrastructure is impacted by security weaknesses by conducting an empirical study of task infections in Ansible scripts. Method We conduct an empirical study where we quantify the frequency of task infections in Ansible scripts. Upon detection of task infections, we apply qualitative analysis to determine task infection categories. We also conduct a survey with 23 practitioners to determine the prevalence and severity of identified task infection categories. With logistic regression analysis, we identify development factors that correlate with presence of task infections. Results In all, we identify 1,805 task infections in 27,213 scripts. We identify six task infection categories: anti-virus, continuous integration, data storage, message broker, networking, and virtualization. From our survey, we observe tasks used to manage data storage infrastructure perceived to have the most severe consequences. We also find three development factors, namely age, minor contributors, and scatteredness to correlate with the presence of task infections. Conclusion Our empirical study shows computing infrastructure managed by Ansible scripts to be impacted by security weaknesses. We conclude the paper by discussing the implications of our findings for practitioners and researchers. 

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3. Lessons from Research to Practice on Writing Better Quality Puppet Scripts

   https://doi.org/10.1109/SANER53432.2022.00019  

   Rahman, Akond; Sharma, Tushar (March 2022, 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER))

   Infrastructure as Code (IaC) scripts, such as Puppet scripts, provide practitioners the opportunity to provision computing infrastructure automatically at scale. Poorly written IaC scripts impact various facets of quality (such as security and maintainability) and, in turn, may lead to serious consequences. Many of the ill-effects can be avoided or rectified easily by following recommendations derived from research and best practices gleaned from experience. While researchers have investigated methods to improve quality aspects of Puppet scripts, such research needs to be summarized and synthesized for industry practitioners. In this article, we summarize recent research in the IaC domain by discussing key quality issues, specifically security and maintainability smells, that may arise in an IaC script. We also mine open-source repositories from three organizations (Mozilla, Openstack, and Wikimedia) and report our observations on the identified smells. Furthermore, we also synthesize recommendations from the literature for software practitioners that could improve the quality of IaC scripts. Software development teams dealing with large computing infrastructure can get benefited from the actionable recommended practices. In addition, researchers in the domain may use this study to find opportunities to improve the state-of-the-art. 

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4. Evaluating the Quality of Open Source Ansible Playbooks: An Executability Perspective

   https://doi.org/10.1145/3663530.3665019  

   Mendis, Pemsith; Reeves, Wilson; Babar, Muhammad Ali; Zhang, Yue; Rahman, Akond (July 2024, ACM)

   Infrastructure as code (IaC) is the practice of automatically managing computing platforms, such as Internet of Things (IoT) platforms. IaC has gained popularity in recent years, yielding a plethora of software artifacts, such as Ansible playbooks that are available on social coding platforms. Despite the availability of open source software (OSS) Ansible playbooks, there is a lack of empirical research on the quality of these playbooks, which can hinder the progress of IaC-related research. To that end, we conduct an empirical study with 2,952 OSS Ansible playbooks where we evaluate the quality of OSS playbooks from the perspective of executability, i.e., if publicly available OSS Ansible playbooks can be executed without failures. From our empirical study, we observe 71.5\% of the mined 2,952 Ansible playbooks cannot be executed as is because of four categories of failures. 

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5. Quality Assurance for Infrastructure Orchestrators: Emerging Results from Ansible

   https://doi.org/10.1109/ICSA-C57050.2023.00073  

   Zhang, Yue; Rahman, Muktadir; Wu, Fan; Rahman, Akond (April 2023, 2023 IEEE 20th International Conference on Software Architecture Companion (ICSA-C))

   Infrastructure as code (IaC) is the practice of automatically managing computing infrastructure at scale. Despite yielding multiple benefits for organizations, the practice of IaC is susceptible to quality concerns, which can lead to large-scale consequences. While researchers have studied quality concerns in IaC manifests, quality aspects of infrastructure orchestrators, i.e., tools that implement the practice of IaC, remain an under-explored area. A systematic investigation of defects in infrastructure orchestrators can help foster further research in the domain of IaC. From our empirical study with 22,445 commits mined from the Ansible infrastructure orchestrator we observe (i) a defect density of 17.9 per KLOC, (ii) 12 categories of Ansible components for which defects appear, and (iii) the ‘Module’ component to include more defects than the other 11 components. Based on our empirical study, we provide recommendations for researchers to conduct future research to enhance the quality of infrastructure orchestrators. 

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