Machine Learning-based Models for Predicting Defective Packages

Software defects are often expensive to fix, especially when they are identified late in development. Packages encapsulate logical functionality and are often developed by particular teams. Package-level defect prediction provides insights into defective designs or implementations in a system early. However, there is little work studying how to build prediction models at the package level. In this paper, we develop prediction models by using seven machine-learning algorithms and code metrics. After evaluating our approach on 20 open-source projects, we have presented that we can build effective models for predicting defective packages by using an appropriate set of metrics. However, there is no single set of metrics that can be generalized across all projects. Our study demonstrates the potential for machine-learning models to enable effective package-level defect prediction. This can guide testing and quality assurance to efficiently locate and fix defects.