Predict Software Failure-prone by Learning Bayesian Network

We explore the software metrics and build a Bayesian Network Model for defect prediction. Much previous work has concentrated on how to select the software metrics that are most likely to indicate fault-proneness, based on the hypnosis that these metrics are independent. But in reality, software metric values are predicted not only correlated with fault-proneness, but also observed internal complex relationship with each other. In this paper, we build a Bayesian network model to represent the probability distribution of each factor and how they affect defects, considering strong or weak correlations are existed between individual metric attributes. We perform a comparative experimental study of effectiveness of Bayesian Network, logistic regression and Naive Bayes on a public data set from an open source software system. The result shows that our approach produces statistically significant estimations.