Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/2300
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dc.contributor.authorYeresime, Suresh-
dc.contributor.authorKumar, Lov-
dc.contributor.authorRath, S K-
dc.date.accessioned2015-04-17T15:25:53Z-
dc.date.available2015-04-17T15:25:53Z-
dc.date.issued2014-03-04-
dc.identifier.citationInternational Scholarly Research Notices (ISRN) Software Engineering Volume 2014,15 pages,2014.Article ID 251083en_US
dc.identifier.urihttp://dx.doi.org/10.1155/2014/251083-
dc.identifier.urihttp://hdl.handle.net/2080/2300-
dc.description.abstractExperimental validation of software metrics in fault prediction for object-oriented methods using statistical and machine learning methods is necessary. By the process of validation the quality of software product in a software organization is ensured. Objectoriented metrics play a crucial role in predicting faults.This paper examines the application of linear regression, logistic regression, and artificial neural network methods for software fault prediction using Chidamber and Kemerer (CK) metrics. Here, fault is considered as dependent variable andCKmetric suite as independent variables. Statistical methods such as linear regression, logistic regression, and machine learning methods such as neural network (and its different forms) are being applied for detecting faults associated with the classes. The comparison approach was applied for a case study, that is, Apache integration framework (AIF) version 1.6. The analysis highlights the significance of weighted method per class (WMC) metric for fault classification, and also the analysis shows that the hybrid approach of radial basis function network obtained better fault prediction rate when compared with other three neural network modelsen_US
dc.language.isoenen_US
dc.publisherHindawi Publishing Corporationen_US
dc.subjectStatisticalen_US
dc.subjectMachine Learning Methodsen_US
dc.subjectCK Metric Suiteen_US
dc.subjectFault Predictionen_US
dc.titleStatistical and Machine Learning Methods for Software Fault Prediction Using CK Metric Suite: A Comparative Analysisen_US
dc.typeArticleen_US
Appears in Collections:Journal Articles

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