Machine learning based predictive modeling to effectively implement DevOps practices in software organizations

Development and Operations (DevOps) is a relatively recent phenomenon that can be defined as a multidisciplinary effort to improve and accelerate the delivery of business values in terms of IT solutions. Many software organizations are heading towards DevOps to leverage its benefits in terms of improved development speed, stability, collaboration, and communication. DevOps practices are essential to effectively implement in software organizations, but little attention has been given in the literature to how these practices can be managed. Our study aims to propose and develop a framework for effectively managing DevOps practices. We have conducted an empirical study using the publicly available HELENA2 dataset to identify the best practices for effectively implementing DevOps. Furthermore, we have used the prediction algorithms such as Support Vector Machine (SVM), Artificial Neural Network (ANN) and Random Forest (RF) to develop a prediction model for DevOps implementation. The findings of this study show that “Continuous deployment”, “Coding standards”, “Continuous integration”, and “Daily Standup” "are the most significant practicesduring the life cycle of projects for effectively managing the DevOps practices. The contribution of this study is not only limited to investigating the best DevOps practices but also provides a prediction of DevOps project success and prioritization of best practices. It can assist software organizations in getting the best possible practices to focus on based on the nature of their projects.