Predictive maintenance of infrastructure code using “fluid” datasets: An exploratory study on Ansible defect proneness

This work consolidates and compounds previous investigations in recognizing defects for infrastructure-as-code (IaC) scripts using general software development quality metrics with a focus on defect severity but adding to previous work an explorative look at creating datasets, which may boost the predictive power of provided models-we call this notion a fluid dataset. More specifically, we experiment with 50 different metrics harnessing a multiple dataset creation process whereby different versions of the same datasets are rigged with auto-training facilities for model retraining and redeployment in a DataOps fashion. At this point, with a focus on the Ansible infrastructure code language-as a de facto standard for industrial-strength infrastructure code-we build defect prediction models and manage to improve on the state of the art by finding an F1 score of 0.52 and a recall of 0.57 using a Naive-Bayes classifier. On the one hand, by improving state-of-the-art defect prediction models using metrics generalizable for different IaC languages, we provide interesting leads for the future of infrastructure-as-code. On the other hand, we have barely scratched the surface on the novel approach of fluid-datasets creation and automated retraining of Machine Learning (ML) defect prediction models, warranting for more research on the same direction in the future.