research-article

Empirical analysis of object-oriented design metrics for predicting high, medium and low severity faults using mallows Cp

Authors:

Rajender Singh Chhillar,

NishaAuthors Info & Claims

ACM SIGSOFT Software Engineering Notes, Volume 36, Issue 6

Pages 1 - 9

https://doi.org/10.1145/2047414.2047423

Published: 14 November 2011 Publication History

Abstract

An object-oriented approach has become a commonly-used method in software-related activities. Many design metrics for object-oriented systems have been proposed and also employed for predicting and managing the quality of processes and products. To enhance the practical utility of object-oriented metrics in software industry, various researchers have tried to find relations between these metrics and fault proneness, but very few focus on relating them with the number-offaults in different levels as per their severity rating. In this study, empirical validation is carried out on object-oriented design metrics (i.e. Chidamber and Kemerer CK-metrics suite and source lines of codes) for predicting number-of-faults in different severity levels. Different statistical methods are used to analyze the data, including correlation.

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Cited By

ElGhondakly RMoussa SBadr N(2023)Service-oriented model-based fault prediction and localization for service compositions testing using deep learning techniquesApplied Soft Computing10.1016/j.asoc.2023.110430143:COnline publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1016/j.asoc.2023.110430
Child MRosner PCounsell S(2019)A comparison and evaluation of variants in the coupling between objects metricJournal of Systems and Software10.1016/j.jss.2019.02.020151(120-132)Online publication date: May-2019
https://doi.org/10.1016/j.jss.2019.02.020
Khari MKumar P(2017)Evolutionary Computation-Based Techniques Over Multiple Data Sets: An Empirical AssessmentArabian Journal for Science and Engineering10.1007/s13369-017-2653-543:8(3875-3885)Online publication date: 17-Jun-2017
https://doi.org/10.1007/s13369-017-2653-5
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Index Terms

Empirical analysis of object-oriented design metrics for predicting high, medium and low severity faults using mallows Cp
1. General and reference
  1. Cross-computing tools and techniques
    1. Metrics

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Published In

cover image ACM SIGSOFT Software Engineering Notes

ACM SIGSOFT Software Engineering Notes Volume 36, Issue 6

November 2011

117 pages

ISSN:0163-5948

DOI:10.1145/2047414

Issue’s Table of Contents

Copyright © 2011 Authors.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 November 2011

Published in SIGSOFT Volume 36, Issue 6

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Cited By

ElGhondakly RMoussa SBadr N(2023)Service-oriented model-based fault prediction and localization for service compositions testing using deep learning techniquesApplied Soft Computing10.1016/j.asoc.2023.110430143:COnline publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1016/j.asoc.2023.110430
Child MRosner PCounsell S(2019)A comparison and evaluation of variants in the coupling between objects metricJournal of Systems and Software10.1016/j.jss.2019.02.020151(120-132)Online publication date: May-2019
https://doi.org/10.1016/j.jss.2019.02.020
Khari MKumar P(2017)Evolutionary Computation-Based Techniques Over Multiple Data Sets: An Empirical AssessmentArabian Journal for Science and Engineering10.1007/s13369-017-2653-543:8(3875-3885)Online publication date: 17-Jun-2017
https://doi.org/10.1007/s13369-017-2653-5
Lee SLi DLi Y(2016)An Investigation of Essential Topics on Software Fault-Proneness Prediction2016 International Symposium on System and Software Reliability (ISSSR)10.1109/ISSSR.2016.016(37-46)Online publication date: Oct-2016
https://doi.org/10.1109/ISSSR.2016.016
Chen LMa WZhou YXu LWang ZChen ZXu B(2016)Empirical analysis of network measures for predicting high severity software faultsScience China Information Sciences10.1007/s11432-015-5426-359:12Online publication date: 7-Nov-2016
https://doi.org/10.1007/s11432-015-5426-3
Guo QChen TChen YWang RChen HHu WChen G(2014)Pre-Silicon Bug ForecastIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2013.228868833:3(451-463)Online publication date: 1-Mar-2014
https://dl.acm.org/doi/10.1109/TCAD.2013.2288688
Galinac Grbac TRuneson PHuljenic D(2013)A Second Replicated Quantitative Analysis of Fault Distributions in Complex Software SystemsIEEE Transactions on Software Engineering10.1109/TSE.2012.4639:4(462-476)Online publication date: 1-Apr-2013
https://dl.acm.org/doi/10.1109/TSE.2012.46

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