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Identifying influential metrics in the combined metrics approach of fault prediction

Fault prediction is a pre-eminent area of empirical software engineering which has witnessed a huge surge over the last couple of decades. In the development of a fault prediction model, combination of metrics results in better explanatory power of the model. Since the metrics used in combination ar...

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Detalles Bibliográficos
Autores principales: Goyal, Rinkaj, Chandra, Pravin, Singh, Yogesh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3853189/
https://www.ncbi.nlm.nih.gov/pubmed/24324926
http://dx.doi.org/10.1186/2193-1801-2-627
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author Goyal, Rinkaj
Chandra, Pravin
Singh, Yogesh
author_facet Goyal, Rinkaj
Chandra, Pravin
Singh, Yogesh
author_sort Goyal, Rinkaj
collection PubMed
description Fault prediction is a pre-eminent area of empirical software engineering which has witnessed a huge surge over the last couple of decades. In the development of a fault prediction model, combination of metrics results in better explanatory power of the model. Since the metrics used in combination are often correlated, and do not have an additive effect, the impact of a metric on another i.e. interaction should be taken into account. The effect of interaction in developing regression based fault prediction models is uncommon in software engineering; however two terms and three term interactions are analyzed in detail in social and behavioral sciences. Beyond three terms interactions are scarce, because interaction effects at such a high level are difficult to interpret. From our earlier findings (Softw Qual Prof 15(3):15-23) we statistically establish the pertinence of considering the interaction between metrics resulting in a considerable improvement in the explanatory power of the corresponding predictive model. However, in the aforesaid approach, the number of variables involved in fault prediction also shows a simultaneous increment with interaction. Furthermore, the interacting variables do not contribute equally to the prediction capability of the model. This study contributes towards the development of an efficient predictive model involving interaction among predictive variables with a reduced set of influential terms, obtained by applying stepwise regression.
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spelling pubmed-38531892013-12-09 Identifying influential metrics in the combined metrics approach of fault prediction Goyal, Rinkaj Chandra, Pravin Singh, Yogesh Springerplus Short Report Fault prediction is a pre-eminent area of empirical software engineering which has witnessed a huge surge over the last couple of decades. In the development of a fault prediction model, combination of metrics results in better explanatory power of the model. Since the metrics used in combination are often correlated, and do not have an additive effect, the impact of a metric on another i.e. interaction should be taken into account. The effect of interaction in developing regression based fault prediction models is uncommon in software engineering; however two terms and three term interactions are analyzed in detail in social and behavioral sciences. Beyond three terms interactions are scarce, because interaction effects at such a high level are difficult to interpret. From our earlier findings (Softw Qual Prof 15(3):15-23) we statistically establish the pertinence of considering the interaction between metrics resulting in a considerable improvement in the explanatory power of the corresponding predictive model. However, in the aforesaid approach, the number of variables involved in fault prediction also shows a simultaneous increment with interaction. Furthermore, the interacting variables do not contribute equally to the prediction capability of the model. This study contributes towards the development of an efficient predictive model involving interaction among predictive variables with a reduced set of influential terms, obtained by applying stepwise regression. Springer International Publishing 2013-11-23 /pmc/articles/PMC3853189/ /pubmed/24324926 http://dx.doi.org/10.1186/2193-1801-2-627 Text en © Goyal et al.; licensee Springer. 2013 This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Short Report
Goyal, Rinkaj
Chandra, Pravin
Singh, Yogesh
Identifying influential metrics in the combined metrics approach of fault prediction
title Identifying influential metrics in the combined metrics approach of fault prediction
title_full Identifying influential metrics in the combined metrics approach of fault prediction
title_fullStr Identifying influential metrics in the combined metrics approach of fault prediction
title_full_unstemmed Identifying influential metrics in the combined metrics approach of fault prediction
title_short Identifying influential metrics in the combined metrics approach of fault prediction
title_sort identifying influential metrics in the combined metrics approach of fault prediction
topic Short Report
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3853189/
https://www.ncbi.nlm.nih.gov/pubmed/24324926
http://dx.doi.org/10.1186/2193-1801-2-627
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