NDLI: Software Defect Prediction via Deep Belief Network

Content Provider	IET Digital Library
Author	I, Hua W E N, Chun S H A U, Changzhen H G, Yu Z H A N U, Xiao Y
Abstract	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).
Starting Page	925
Ending Page	932
Page Count	8
ISSN	10224653
Volume Number	28
e-ISSN	20755597
Issue Number	Issue 5, Sep (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/cje/28/5
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/cje.2019.06.012
Journal	Chinese Journal of Electronics
Publisher Date	2019-09-01
Access Restriction	Open
Rights Holder	© Chinese Institute of Electronics
Subject Keyword	Accurate Prediction Model Belief Network Computer Vision And Image Processing Technique Data Characteristic Data Handling Technique Data Loss Data Redundancy DBNPM Deep Belief Network Prediction Model Deep Belief Network Technology Defect Distribution Prediction Model Effective Deep Learning Technique Fundamental Cause Knowledge Engineering Technique Learning in AI Meaningful Topic Natural Language Processing Pattern Classification Prediction Effect Previous Researches Focus Program Module Software Defect Data Software Defect Prediction Software Defects Software Engineering Software Reliability Source Program Statistics Support Vector Machine Testing Efforts
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Electrical and Electronic Engineering

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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