An Approach to Identifying Error Patterns for Infrastructure as Code
Wei Chen, Guoquan Wu, Jun Fang Wei · 2018
Infrastructure as Code (IaC), which specifies system configurations in an imperative or declarative way, automates environment set up, system deployment and configuration. Despite wide adoption, developing and maintaining high-quality IaC artifacts is still challenging. This paper proposes an approach to handling the fine-grained and frequently occurring IaC code errors. The approach extracts code changes from historical commits and clusters them into groups, by constructing a feature model of code changes and employing an unsupervised machine learning algorithm. It identifies error patterns from the clusters and proposes a set of inspection rules to check the potential IaC code errors. In practice, we take Puppet code artifacts as subject objects and perform a comprehensive study on 14 popular Puppet artifacts. In our experiment, we get 41 cross-artifact error patterns, covering 42% crawled code changes. Based on these patterns, 30 rules are proposed, covering 60% identified error patterns, to proactively check IaC artifacts. The approach would be helpful in improving code quality of IaC artifacts.