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Empirical Studies to Predict Fault Proneness: A Review

by Puja Saxena, Monika Saini
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 22 - Number 8
Year of Publication: 2011
Authors: Puja Saxena, Monika Saini
10.5120/2600-3626

Puja Saxena, Monika Saini . Empirical Studies to Predict Fault Proneness: A Review. International Journal of Computer Applications. 22, 8 ( May 2011), 41-45. DOI=10.5120/2600-3626

@article{ 10.5120/2600-3626,
author = { Puja Saxena, Monika Saini },
title = { Empirical Studies to Predict Fault Proneness: A Review },
journal = { International Journal of Computer Applications },
issue_date = { May 2011 },
volume = { 22 },
number = { 8 },
month = { May },
year = { 2011 },
issn = { 0975-8887 },
pages = { 41-45 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume22/number8/2600-3626/ },
doi = { 10.5120/2600-3626 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:08:54.161381+05:30
%A Puja Saxena
%A Monika Saini
%T Empirical Studies to Predict Fault Proneness: A Review
%J International Journal of Computer Applications
%@ 0975-8887
%V 22
%N 8
%P 41-45
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Empirical validations of software metrics are used to predict software quality in the past years. This paper provides a review of empirical studies to predict software fault proneness with a specific focus on techniques used. The paper highlights the milestone studies done from 1995 to 2010 in this area. Results show that use of machine learning languages have started.This paper reviews works done in the field of software fault prediction studies. The study is concentrated on statistical techniques and their usage to predict fault proneness. The conclusion drawn is the future studies should use more of class level metrics and the best technique to derive fault predictors amongst statistical techniques is logistic regression.

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Index Terms

Computer Science
Information Sciences

Keywords

Object Oriented Metrics Fault Proneness

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