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Software Defect Prediction via Deep Belief Network

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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).

Defect predictionSoftware securityDeep belief network(DBN)

WEI Hua、SHAN Chun、HU Changzhen、ZHANG Yu、YU Xiao

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School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China

China Information Technology Security Evaluation Center, Beijing 100085, China

Beijing Key Laboratory of Software Security Engineering Technology, Beijing Institute of Technology,Beijing 100081, China

School of Electrical and Information Engineering and Beijing Key Laboratory of Intelligent Processing for Building Big Data, Beijing University of Civil Engineering and Architecture, Beijing 100044, China

State Key Laboratory in China for Geomechanics and Deep Underground Engineering (Beijing), China University of Mining and Technology, Beijing 100083, China

School of Computer Science and Technology, Shandong University of Technology, Zibo 255022, China

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This work is supported by the National Natural Science Foundation of ChinaThis work is supported by the National Natural Science Foundation of ChinaFundamental Research Funds for Beijing Universities of Civil Engineering and Architecture (Response by ZhangYu)Excellent Teachers Development Foundation of BUCEA (Response by ZhangYu)National Key R&D Program of China

U1636115618760192016YFC060090

2019

10.1049/cje.2019.06.012

中国电子杂志(英文版)

中国电子杂志(英文版)

CSTPCDCSCDSCIEI
ISSN：1022-4653
年,卷(期)：2019.28(5)
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