A novel Bayes defect predictor based on information diffusion function

Abstract Software defect prediction plays a significant part in identifying the most defect-prone modules before software testing. Quite a number of researchers have made great efforts to improve prediction accuracy. However, the problem of insufficient historical data available for within- or cross- project still remains unresolved. Further, it is common practice to use the probability density function for a normal distribution in Naive Bayes (NB) classifier. Nevertheless, after performing a Kolmogorov–Smirnov test, we find that the 21 main software metrics are not normally distributed at the 5% significance level. Therefore, this paper proposes a new Bayes classifier, which evolves NB classifier with non-normal information diffusion function, to help solve the problem of lacking appropriate training data for new projects. We conduct three experiments on 34 data sets obtained from 10 open source projects, using only 10%, 6.67%, 5%, 3.33% and 2% of the total data for training, respectively. Four well-known classification algorithms are also included for comparison, namely Logistic Regression, Naive Bayes, Random Tree and Support Vector Machine. All experimental results demonstrate the efficiency and practicability of the new classifier.

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