research-article

Prediction of software fault-prone classes using an unsupervised hybrid SOM algorithm

Authors:

S. DuraisamyAuthors Info & Claims

Volume 22, Issue 1

Pages 133 - 143

https://doi.org/10.1007/s10586-018-1923-7

Published: 01 January 2019 Publication History

Abstract

In software engineering fault proneness prediction is one of the important fields for quality measurement using multiple code metrics. The metrics thresholds are very practical in measuring the code quality for fault proneness prediction. It helps to improvise the software quality in short time with very low cost. Many researchers are in the race to develop a measuring attribute for the software quality using various methodologies. Currently so many fault proneness prediction models are available. Among that most of the methods are used to identify the faults either by data history or by special supervising algorithms. In most of the real time cases the fault data bases may not be available so that the process becomes tedious. This article proposes a hybrid model for identifying the faults in the software models and also we proposed coupling model along with the algorithm so that the metrics are used to identify the faults and the coupling model couples the metrics and the faults for the developed system software.

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Cited By

Balaram AVasundra S(2022)Prediction of software fault-prone classes using ensemble random forest with adaptive synthetic sampling algorithmAutomated Software Engineering10.1007/s10515-021-00311-z29:1Online publication date: 1-May-2022
https://dl.acm.org/doi/10.1007/s10515-021-00311-z
Jin C(2020)Software defect prediction model based on distance metric learningSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-020-05159-125:1(447-461)Online publication date: 13-Jul-2020
https://dl.acm.org/doi/10.1007/s00500-020-05159-1

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Prediction of software fault-prone classes using an unsupervised hybrid SOM algorithm

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Published In

cover image Cluster Computing

Cluster Computing Volume 22, Issue 1

Jan 2019

2608 pages

ISSN:1386-7857

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Copyright © 2019 Springer Science+Business Media, LLC, part of Springer Nature.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 January 2019

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Cited By

Balaram AVasundra S(2022)Prediction of software fault-prone classes using ensemble random forest with adaptive synthetic sampling algorithmAutomated Software Engineering10.1007/s10515-021-00311-z29:1Online publication date: 1-May-2022
https://dl.acm.org/doi/10.1007/s10515-021-00311-z
Jin C(2020)Software defect prediction model based on distance metric learningSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-020-05159-125:1(447-461)Online publication date: 13-Jul-2020
https://dl.acm.org/doi/10.1007/s00500-020-05159-1

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