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Infrastructure-as-code (IaC) is the DevOps practice enabling management and provisioning of infrastructure through the definition of machine-readable files, hereinafter referred to as <italic>IaC scripts</italic>. Similarly to other source code artefacts, these files may contain defects that can preclude their correct functioning. In this paper, we aim at assessing the role of <italic>product</italic> and <italic>process</italic> metrics when predicting defective IaC scripts. We propose a fully integrated machine-learning framework for IaC Defect Prediction, that allows for repository crawling, metrics collection, model building, and evaluation. To evaluate it, we analyzed 104 projects and employed five machine-learning classifiers to compare their performance in flagging suspicious defective IaC scripts. The key results of the study report <sc>Random Forest</sc> as the best-performing model, with a median AUC-PR of 0.93 and MCC of 0.80. Furthermore, at least for the collected projects, product metrics identify defective IaC scripts more accurately than process metrics. Our findings put a baseline for investigating IaC Defect Prediction and the relationship between the product and process metrics, and IaC scripts&#x2019; quality.

Within-Project Defect Prediction of Infrastructure-as-Code Using Product and Process Metrics