A Probabilistic Approach to Building Defect Prediction Model for Platform-based Product Lines

Abstract

Determining when software testing should be begun and the resources that may be required to find and fix defects is complicated. Being able to predict the number of defects for an upcoming software product given the current development team enables the project managers to make better decisions. A majority of reported defects are managed and tracked using a repository system, which tracks a defect throughout its lifetime. The Defect Life Cycle (DLC) begins when a defect is found and ends when the resolution is verified and the defect is closed. Defects transition through different states according to the evolution of the project, which involves testing, debugging, and verification. All of these defect transitions should be logged using the Defect Tracking Systems (DTS). We construct a Markov chain theory-based defect prediction model for consecutive software products using defect transition history. During model construction, the state of each defect is modelled using the DLC states. The proposed model can predict the defect trends such as total number of defects and defect distribution states in the consecutive products. The model is evaluated using an actual industrial mobile product software project and found to be well suited for the selected domain.