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Vovel metrics—novel coupling metrics for improved software fault prediction

Software is a complex entity, and its development needs careful planning and a high amount of time and cost. To assess quality of program, software measures are very helpful. Amongst the existing measures, coupling is an important design measure, which computes the degree of interdependence among th...

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Autores principales: Muhammad, Rizwan, Nadeem, Aamer, Azam Sindhu, Muddassar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8205299/
https://www.ncbi.nlm.nih.gov/pubmed/34179454
http://dx.doi.org/10.7717/peerj-cs.590
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author Muhammad, Rizwan
Nadeem, Aamer
Azam Sindhu, Muddassar
author_facet Muhammad, Rizwan
Nadeem, Aamer
Azam Sindhu, Muddassar
author_sort Muhammad, Rizwan
collection PubMed
description Software is a complex entity, and its development needs careful planning and a high amount of time and cost. To assess quality of program, software measures are very helpful. Amongst the existing measures, coupling is an important design measure, which computes the degree of interdependence among the entities of a software system. Higher coupling leads to cognitive complexity and thus a higher probability occurrence of faults. Well in time prediction of fault-prone modules assists in saving time and cost of testing. This paper aims to capture important aspects of coupling and then assess the effectiveness of these aspects in determining fault-prone entities in the software system. We propose two coupling metrics, i.e., Vovel-in and Vovel-out, that capture the level of coupling and the volume of information flow. We empirically evaluate the effectiveness of the Vovel metrics in determining the fault-prone classes using five projects, i.e., Eclipse JDT, Equinox framework, Apache Lucene, Mylyn, and Eclipse PDE UI. Model building is done using univariate logistic regression and later Spearman correlation coefficient is computed with the existing coupling metrics to assess the coverage of unique information. Finally, the least correlated metrics are used for building multivariate logistic regression with and without the use of Vovel metrics, to assess the effectiveness of Vovel metrics. The results show the proposed metrics significantly improve the predicting of fault prone classes. Moreover, the proposed metrics cover a significant amount of unique information which is not covered by the existing well-known coupling metrics, i.e., CBO, RFC, Fan-in, and Fan-out. This paper, empirically evaluates the impact of coupling metrics, and more specifically the importance of level and volume of coupling in software fault prediction. The results advocate the prudent addition of proposed metrics due to their unique information coverage and significant predictive ability.
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spelling pubmed-82052992021-06-24 Vovel metrics—novel coupling metrics for improved software fault prediction Muhammad, Rizwan Nadeem, Aamer Azam Sindhu, Muddassar PeerJ Comput Sci Artificial Intelligence Software is a complex entity, and its development needs careful planning and a high amount of time and cost. To assess quality of program, software measures are very helpful. Amongst the existing measures, coupling is an important design measure, which computes the degree of interdependence among the entities of a software system. Higher coupling leads to cognitive complexity and thus a higher probability occurrence of faults. Well in time prediction of fault-prone modules assists in saving time and cost of testing. This paper aims to capture important aspects of coupling and then assess the effectiveness of these aspects in determining fault-prone entities in the software system. We propose two coupling metrics, i.e., Vovel-in and Vovel-out, that capture the level of coupling and the volume of information flow. We empirically evaluate the effectiveness of the Vovel metrics in determining the fault-prone classes using five projects, i.e., Eclipse JDT, Equinox framework, Apache Lucene, Mylyn, and Eclipse PDE UI. Model building is done using univariate logistic regression and later Spearman correlation coefficient is computed with the existing coupling metrics to assess the coverage of unique information. Finally, the least correlated metrics are used for building multivariate logistic regression with and without the use of Vovel metrics, to assess the effectiveness of Vovel metrics. The results show the proposed metrics significantly improve the predicting of fault prone classes. Moreover, the proposed metrics cover a significant amount of unique information which is not covered by the existing well-known coupling metrics, i.e., CBO, RFC, Fan-in, and Fan-out. This paper, empirically evaluates the impact of coupling metrics, and more specifically the importance of level and volume of coupling in software fault prediction. The results advocate the prudent addition of proposed metrics due to their unique information coverage and significant predictive ability. PeerJ Inc. 2021-06-10 /pmc/articles/PMC8205299/ /pubmed/34179454 http://dx.doi.org/10.7717/peerj-cs.590 Text en © 2021 Muhammad et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Artificial Intelligence
Muhammad, Rizwan
Nadeem, Aamer
Azam Sindhu, Muddassar
Vovel metrics—novel coupling metrics for improved software fault prediction
title Vovel metrics—novel coupling metrics for improved software fault prediction
title_full Vovel metrics—novel coupling metrics for improved software fault prediction
title_fullStr Vovel metrics—novel coupling metrics for improved software fault prediction
title_full_unstemmed Vovel metrics—novel coupling metrics for improved software fault prediction
title_short Vovel metrics—novel coupling metrics for improved software fault prediction
title_sort vovel metrics—novel coupling metrics for improved software fault prediction
topic Artificial Intelligence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8205299/
https://www.ncbi.nlm.nih.gov/pubmed/34179454
http://dx.doi.org/10.7717/peerj-cs.590
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