A Framework for Measuring the Quality of Infrastructure-as-Code Scripts

2025-02-05

AI Summary

The rapid proliferation of Infrastructure-as-Code (IaC) scripts—particularly Ansible playbooks—lacks systematic, scalable quality assessment methodologies. Method: This paper proposes the first extensible, multi-dimensional, and quantifiable IaC code quality assessment framework. Leveraging over one thousand real-world repositories from Ansible Galaxy, it integrates static analysis, metadata mining, and empirical study to construct a weighted evaluation model across dimensions including error handling, automation level, and documentation completeness. Temporal analysis further uncovers evolutionary trends—e.g., progressive metadata improvement alongside declining automation capability. Contribution/Results: The framework establishes a theoretical foundation for IaC quality standardization and enables practitioners to precisely identify quality bottlenecks, thereby facilitating engineering-driven quality governance in IaC development and maintenance.

Technology Category

• Machine Learning: Evaluation and Analysis
• Knowledge Representation and Reasoning: Qualitative Reasoning
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Infrastructure as Code (IaC) has become integral to modern software development, enabling automated and consistent configuration of computing environments. The rapid proliferation of IaC scripts has highlighted the need for better code quality assessment methods. This paper proposes a new IaC code quality framework specifically showcased for Ansible repositories as a foundation. By analyzing a comprehensive dataset of repositories from Ansible Galaxy, we applied our framework to evaluate code quality across multiple attributes. The analysis of our code quality metrics applied to Ansible Galaxy repositories reveal trends over time indicating improvements in areas such as metadata and error handling, while highlighting declines in others such as sophistication and automation. The framework offers practitioners a systematic tool for assessing and enhancing IaC scripts, fostering standardization and facilitating continuous improvement. It also provides a standardized foundation for further work into IaC code quality.

Problem

• Measuring Infrastructure-as-Code script quality
• Developing Ansible code quality framework
• Analyzing trends in Ansible Galaxy repositories

Innovation

• Framework for IaC quality measurement
• Analysis of Ansible Galaxy repositories
• Systematic tool for script enhancement