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An Efficient Software Fault Prediction Model using Cluster based Classification

by Pradeep Singh, Shrish Verma
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International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 7 - Number 3
Year of Publication: 2014
Authors: Pradeep Singh, Shrish Verma
10.5120/ijais14-451160

Pradeep Singh, Shrish Verma . An Efficient Software Fault Prediction Model using Cluster based Classification. International Journal of Applied Information Systems. 7, 3 ( May 2014), 35-41. DOI=10.5120/ijais14-451160

@article{ 10.5120/ijais14-451160,
author = { Pradeep Singh, Shrish Verma },
title = { An Efficient Software Fault Prediction Model using Cluster based Classification },
journal = { International Journal of Applied Information Systems },
issue_date = { May 2014 },
volume = { 7 },
number = { 3 },
month = { May },
year = { 2014 },
issn = { 2249-0868 },
pages = { 35-41 },
numpages = {9},
url = { https://www.ijais.org/archives/volume7/number3/632-1160/ },
doi = { 10.5120/ijais14-451160 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T18:54:51.099956+05:30
%A Pradeep Singh
%A Shrish Verma
%T An Efficient Software Fault Prediction Model using Cluster based Classification
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 7
%N 3
%P 35-41
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Predicting fault -prone software components is an economically important activity due to limited budget allocation for software testing. In recent years data mining techniques are used to predict the software faults .In this research, we present a cluster based fault prediction classifiers which increases the probability of detection. The expectation from a predictor is to get very high probability of detection to get more reliable and test effective software. In our experiments, we used fault data from mission critical systems. In this paper we have used discretization as preprocessing and cluster based classification for prediction of fault-prone software modules. Clustering based classification allows production of comprehensible models of software faults exploiting symbolic learning algorithms. To evaluate this approach we perform an extensive comparative analysis with benchmark results of software fault prediction for the same data sets. Our proposed model shows better results than the standard and benchmark approaches for software fault prediction. Our proposed model gives superior probability of detection (pd) 83.3% and balance rates 685%.

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Index Terms

Computer Science
Information Sciences

Keywords

Error Prone Software fault prediction software metrics

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