  Predicting the number of defects in a project is critical for project test
managers to allocate budget, resources, and schedule for testing, support and
maintenance efforts. Software Defect Prediction models predict the number of
defects in given projects after training the model with historical defect
related information. The majority of defect prediction studies focused on
predicting defect-prone modules from methods, and class-level static
information, whereas this study predicts defects from project-level information
based on a cross-company project dataset. This study utilizes software sizing
metrics, effort metrics, and defect density information, and focuses on
developing defect prediction models that apply various machine learning
algorithms. One notable issue in existing defect prediction studies is the lack
of transparency in the developed models. Consequently, the explain-ability of
the developed model has been demonstrated using the state-of-the-art post-hoc
model-agnostic method called Shapley Additive exPlanations (SHAP). Finally,
important features for predicting defects from cross-company project
information were identified.
