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The Ultimate Configuration Management Tool? Lessons from a Mixed Methods Study of Ansible's Challenges

Carolina Carreira, Nuno Saavedra, Alexandra Mendes, João F. Ferreira

TL;DR

This study targets the challenges faced by Ansible users within the IaC domain by combining large-scale analyses of 59,157 online posts with 20 semi-structured interviews. Using TopicGPT-based topic modeling, the authors extract 87 topics and validate them, then merge these findings with qualitative codes to reveal seven core challenge areas, including control flow, reliability, and usability, as well as information gaps and maintenance concerns. The work yields concrete recommendations for refactoring, language design, improved debugging, and documentation, and discusses broader implications for IaC tool design, education, and community governance. The replication package and explicit methodology offer a transferable blueprint for evaluating other IaC platforms and guiding future tool evolution toward better usability, scalability, and reliability.

Abstract

Infrastructure as Code (IaC) tools have transformed the way IT infrastructure is automated and managed, but their growing adoption has also exposed numerous challenges for practitioners. In this paper, we investigate these challenges through the lens of Ansible, a popular IaC tool. Using a mixed methods approach, we investigate challenges, obstacles, and issues faced by practitioners. We analyze 59,157 posts from Stack Overflow, Reddit, and the Ansible Forum to identify common pain points, complemented by 20 semi-structured interviews with practitioners of varying expertise levels. Based on our findings, we propose four main recommendations to improve Ansible: 1) refactoring to mitigate performance issues, 2) restructuring higher-level language concepts, 3) improved debugging and error reporting tools, and 4) better documentation and learning resources. By highlighting the real-world struggles of Ansible users, we provide actionable insights for tool designers, educators, and the broader IaC community, contributing to a deeper understanding of the trade-offs inherent in IaC tools.

The Ultimate Configuration Management Tool? Lessons from a Mixed Methods Study of Ansible's Challenges

TL;DR

This study targets the challenges faced by Ansible users within the IaC domain by combining large-scale analyses of 59,157 online posts with 20 semi-structured interviews. Using TopicGPT-based topic modeling, the authors extract 87 topics and validate them, then merge these findings with qualitative codes to reveal seven core challenge areas, including control flow, reliability, and usability, as well as information gaps and maintenance concerns. The work yields concrete recommendations for refactoring, language design, improved debugging, and documentation, and discusses broader implications for IaC tool design, education, and community governance. The replication package and explicit methodology offer a transferable blueprint for evaluating other IaC platforms and guiding future tool evolution toward better usability, scalability, and reliability.

Abstract

Infrastructure as Code (IaC) tools have transformed the way IT infrastructure is automated and managed, but their growing adoption has also exposed numerous challenges for practitioners. In this paper, we investigate these challenges through the lens of Ansible, a popular IaC tool. Using a mixed methods approach, we investigate challenges, obstacles, and issues faced by practitioners. We analyze 59,157 posts from Stack Overflow, Reddit, and the Ansible Forum to identify common pain points, complemented by 20 semi-structured interviews with practitioners of varying expertise levels. Based on our findings, we propose four main recommendations to improve Ansible: 1) refactoring to mitigate performance issues, 2) restructuring higher-level language concepts, 3) improved debugging and error reporting tools, and 4) better documentation and learning resources. By highlighting the real-world struggles of Ansible users, we provide actionable insights for tool designers, educators, and the broader IaC community, contributing to a deeper understanding of the trade-offs inherent in IaC tools.

Paper Structure

This paper contains 55 sections, 3 figures.

Table of Contents

  1. Introduction
  2. Ansible
  3. Mixed Methods Study
  4. Research Questions
  5. Topic Modeling
  6. Data Collection
  7. Topic Generation
  8. Topic Refinement
  9. Topic Assignment
  10. Validation
  11. Analysis
  12. Interviews
  13. Participants
  14. Recruitment
  15. Ethical Considerations
  16. ...and 40 more sections

Figures (3)

  • Figure 1: Excerpts from an Ansible playbook to configure a web server.
  • Figure 2: Overview of the research methodology combining automated topic extraction and thematic analysis.
  • Figure 3: Distribution of posts across topics and their respective themes.