关键词: 软件缺陷预测, 朴素贝叶斯, 直线集合分割, 元分类器, 集成框架

Abstract: Software defect prediction can effectively improve the efficiency of software test. Based on Na?ve Bayes statistical theory, a software defect prediction model named LGD-NB is put forward by using geometrical relationship between a point and a line. The LGD-NB model has two forms; the one is decision-making based on the least risk which has more accurate description to the cost of misclassification. By defining the high risk area of decision-making, the second form of LGD-NB is to be a meta-classifier which provides an ensemble scheme for software defect prediction by which the another classifier can classify the modules which locate at the high risk area. The experimental results show that the LGD-NB based on the least risk has better prediction performance than Na?ve Bayes models. The LGD-NB ensemble with SVM has also effectively improved the prediction performance.

Key words: software defect prediction, Na?Ve Bayes, line geometry division, meta-classifier, ensemble scheme