Visible to the public Cross-Project Defect Prediction Using a Credibility Theory Based Naive Bayes Classifier

TitleCross-Project Defect Prediction Using a Credibility Theory Based Naive Bayes Classifier
Publication TypeConference Paper
Year of Publication2017
AuthorsPoon, W. N., Bennin, K. E., Huang, J., Phannachitta, P., Keung, J. W.
Conference Name2017 IEEE International Conference on Software Quality, Reliability and Security (QRS)
ISBN Number978-1-5386-0592-9
KeywordsBayes methods, Computer science, CPDP, credibility theory, credibility theory based naive Bayes classifier, cross-project defect prediction, Insurance, Metrics, Nai¨ve Bayes classifier, novel reweighting mechanism, pattern classification, Predictive Metrics, Predictive models, predictive security metrics, program debugging, pubcrawl, quality assurance, Software, software engineering, software quality, source projects, Standards, target data distribution, target projects, Training, Training data, transfer learning

Several defect prediction models proposed are effective when historical datasets are available. Defect prediction becomes difficult when no historical data exist. Cross-project defect prediction (CPDP), which uses projects from other sources/companies to predict the defects in the target projects proposed in recent studies has shown promising results. However, the performance of most CPDP approaches are still beyond satisfactory mainly due to distribution mismatch between the source and target projects. In this study, a credibility theory based Naive Bayes (CNB) classifier is proposed to establish a novel reweighting mechanism between the source projects and target projects so that the source data could simultaneously adapt to the target data distribution and retain its own pattern. Our experimental results show that the feasibility of the novel algorithm design and demonstrate the significant improvement in terms of the performance metrics considered achieved by CNB over other CPDP approaches.

Citation Keypoon_cross-project_2017