关键词: 缺陷预测, 贝叶斯Logistic回归, 信息先验

Abstract: Software defect prediction can help project management team to optimize development and test resources in time, so as to carry out strict quality assurance activities for software modules that may contain defects, which plays an important role in the high-quality delivery of software. Therefore, software defect prediction has become a research hot topic in the field of software engineering. Though defect prediction models have been built using several machine learning algorithms, Bayesian approach of these models is not explored. Bayesian Logistic regression method with non-informative and informative priors is proposed to build defect prediction models. The advantages of Bayesian Logistic regression and the role of priors in the performance of Bayesian logistic regression are studied. Finally, compared with other existing defect prediction methods（LR, NB, RF, SVM） on PROMISE dataset, the results show that Bayesian Logistic regression method can achieve good prediction performance.

Key words: software defect prediction, Bayesian Logistic regression, informative priors