Software defect prediction using Bayesian networks
Empirical Software Engineering
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Software project development is a risky process with high failure rate. This paper proposes an intelligent model that can predict and control software development risks from an overall project perspective rather than focusing only on the single factor, project output. In this study, we first constructed a formal model for risk identification, and then collected actual cases from software development companies to build a risk prediction model. In order to evaluate the performance of our model, two machine learning algorithms, Artificial Neural Networks (ANN) and Support Vector Machine (SVM), are compared. The experiments show that our risk prediction model based on SVM achieves better performance in prediction.