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Software Defect Prediction Tool based on Neural Network

by Malkit Singh, Dalwinder Singh Salaria
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 70 - Number 22
Year of Publication: 2013
Authors: Malkit Singh, Dalwinder Singh Salaria
10.5120/12200-8368

Malkit Singh, Dalwinder Singh Salaria . Software Defect Prediction Tool based on Neural Network. International Journal of Computer Applications. 70, 22 ( May 2013), 22-28. DOI=10.5120/12200-8368

@article{ 10.5120/12200-8368,
author = { Malkit Singh, Dalwinder Singh Salaria },
title = { Software Defect Prediction Tool based on Neural Network },
journal = { International Journal of Computer Applications },
issue_date = { May 2013 },
volume = { 70 },
number = { 22 },
month = { May },
year = { 2013 },
issn = { 0975-8887 },
pages = { 22-28 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume70/number22/12200-8368/ },
doi = { 10.5120/12200-8368 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:33:33.281878+05:30
%A Malkit Singh
%A Dalwinder Singh Salaria
%T Software Defect Prediction Tool based on Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 70
%N 22
%P 22-28
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

There has been a tremendous growth in the demand for software fault prediction during recent years. In this paper, Levenberg-Marquardt (LM) algorithm based neural network tool is used for the prediction of software defects at an early stage of the software development life cycle. It helps to minimize the cost of testing which minimizes the cost of the project. The methods, metrics and datasets are used to find the fault proneness of the software. The study used data collected from the PROMISE repository of empirical software engineering data. This dataset uses the CK (Chidamber and Kemerer) OO (object-oriented) metrics. The accuracy of Levenberg-Marquardt (LM) algorithm based neural network are comparing with the polynomial function-based neural network predictors for detection of software defects. Our results indicate that the prediction model has a high accuracy.

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Index Terms

Computer Science
Information Sciences

Keywords

Defect prediction Metrics Neural network Dataset Levenberg-Marquardt (LM) algorithm

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