Continuous Defect Prediction in CI/CD Pipelines: A Machine Learning-Based Framework

Recent advances in information technology has led to an increasing number of applications to be developed and maintained daily by product teams. Ensuring that a software application works as expected and that it is absent of bugs requires a lot of time and resources. Thanks to the recent adoption of DevOps methodologies, it is often the case where code commits and application builds are centralized and standardized. Thanks to this new approach, it is now possible to retrieve log and build data to ease the development and management operations of product teams. However, even if such approaches include code control to detect unit or integration errors, they do not check for the presence of logical bugs that can raise after code builds. For such reasons in this work we propose a framework for continuous defect prediction based on machine learning algorithms trained on a publicly available dataset. The framework is composed of a machine learning model for detecting the presence of logical bugs in code on the basis of the available data generated by DevOps tools and a dashboard to monitor the software projects status. We also describe the serverless architecture we designed for hosting the aforementioned framework.