Software defect prediction via deep belief network

Defect distribution prediction is a meaningful topic because software defects are the fundamental cause of many attacks and data loss. Building accurate prediction models can help developers find bugs and prioritize their testing efforts. Previous researches focus on exploring different machine learning algorithms based on the features that encode the characteristics of programs. The problem of data redundancy exists in software defect data set, which has great influence on prediction effect. We propose a defect distribution prediction model (Deep belief network prediction model, DBNPM), a system for detecting whether a program module contains defects. The key insight of DBNPM is Deep belief network (DBN) technology, which is an effective deep learning technique in image processing and natural language processing, whose features are similar to defects in source program. Experiment results show that DBNPM can efficiently extract and process the data characteristics of source program and the performance is better than Support vector machine (SVM), Locally linear embedding SVM (LLE-SVM), and Neighborhood preserving embedding SVM (NPE-SVM).