A study of subgroup discovery approaches for defect prediction

Context: Although many papers have been published on software defect prediction techniques, machine learning approaches have yet to be fully explored. Objective: In this paper we suggest using a descriptive approach for defect prediction rather than the pre-cise classification techniques that are us...

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Autores: Rodríguez, Daniel, Ruiz, Roberto, Riquelme Santos, José Cristóbal, Harrison, Rachel
Tipo de recurso: artículo
Estado:Versión enviada para evaluación y publicación
Fecha de publicación:2013
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/43511
Acceso en línea:http://hdl.handle.net/11441/43511
https://doi.org/10.1016/j.infsof.2013.05.002
Access Level:acceso abierto
Palabra clave:Subgroup discovery
Rules
Defect Prediction
Imbalanced datasets
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repository_id_str
spelling A study of subgroup discovery approaches for defect predictionRodríguez, DanielRuiz, RobertoRiquelme Santos, José CristóbalHarrison, RachelSubgroup discoveryRulesDefect PredictionImbalanced datasetsContext: Although many papers have been published on software defect prediction techniques, machine learning approaches have yet to be fully explored. Objective: In this paper we suggest using a descriptive approach for defect prediction rather than the pre-cise classification techniques that are usually adopted. This allows us to characterise defective modules with simple rules that can easily be applied by practitioners and deliver a practical (or engineering) approach rather than a highly accurate result. Method: We describe two well-known subgroup discovery algorithms, the SD algorithm and the CN2-SD algorithm to obtain rules that identify defect prone modules. The empirical work is performed with pub-licly available datasets from the Promise repository and object-oriented metrics from an Eclipse reposi-tory related to defect prediction. Subgroup discovery algorithms mitigate against characteristics of datasets that hinder the applicability of classification algorithms and so remove the need for preprocess-ing techniques. Results: The results show that the generated rules can be used to guide testing effort in order to improve the quality of software development projects. Such rules can indicate metrics, their threshold values and relationships between metrics of defective modules. Conclusions: The induced rules are simple to use and easy to understand as they provide a description rather than a complete classification of the whole dataset. Thus this paper represents an engineering approach to defect prediction, i.e., an approach which is useful in practice, easily understandable and can be applied by practitioners.ICEBERG IAPP-2012-324356MICINN TIN2011-28956-C02-00ElsevierLenguajes y Sistemas InformáticosMinisterio de Ciencia e Innovación (MICIN). España2013info:eu-repo/semantics/articleinfo:eu-repo/semantics/submittedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/11441/43511https://doi.org/10.1016/j.infsof.2013.05.002reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésInformation and Software Technology, 55 (10), 1810-1822.IAPP-2012-324356TIN2011-28956-C02-00http://dx.doi.org/10.1016/j.infsof.2013.05.002info:eu-repo/semantics/openAccessoai:idus.us.es:11441/435112026-06-17T12:51:07Z
dc.title.none.fl_str_mv A study of subgroup discovery approaches for defect prediction
title A study of subgroup discovery approaches for defect prediction
spellingShingle A study of subgroup discovery approaches for defect prediction
Rodríguez, Daniel
Subgroup discovery
Rules
Defect Prediction
Imbalanced datasets
title_short A study of subgroup discovery approaches for defect prediction
title_full A study of subgroup discovery approaches for defect prediction
title_fullStr A study of subgroup discovery approaches for defect prediction
title_full_unstemmed A study of subgroup discovery approaches for defect prediction
title_sort A study of subgroup discovery approaches for defect prediction
dc.creator.none.fl_str_mv Rodríguez, Daniel
Ruiz, Roberto
Riquelme Santos, José Cristóbal
Harrison, Rachel
author Rodríguez, Daniel
author_facet Rodríguez, Daniel
Ruiz, Roberto
Riquelme Santos, José Cristóbal
Harrison, Rachel
author_role author
author2 Ruiz, Roberto
Riquelme Santos, José Cristóbal
Harrison, Rachel
author2_role author
author
author
dc.contributor.none.fl_str_mv Lenguajes y Sistemas Informáticos
Ministerio de Ciencia e Innovación (MICIN). España
dc.subject.none.fl_str_mv Subgroup discovery
Rules
Defect Prediction
Imbalanced datasets
topic Subgroup discovery
Rules
Defect Prediction
Imbalanced datasets
description Context: Although many papers have been published on software defect prediction techniques, machine learning approaches have yet to be fully explored. Objective: In this paper we suggest using a descriptive approach for defect prediction rather than the pre-cise classification techniques that are usually adopted. This allows us to characterise defective modules with simple rules that can easily be applied by practitioners and deliver a practical (or engineering) approach rather than a highly accurate result. Method: We describe two well-known subgroup discovery algorithms, the SD algorithm and the CN2-SD algorithm to obtain rules that identify defect prone modules. The empirical work is performed with pub-licly available datasets from the Promise repository and object-oriented metrics from an Eclipse reposi-tory related to defect prediction. Subgroup discovery algorithms mitigate against characteristics of datasets that hinder the applicability of classification algorithms and so remove the need for preprocess-ing techniques. Results: The results show that the generated rules can be used to guide testing effort in order to improve the quality of software development projects. Such rules can indicate metrics, their threshold values and relationships between metrics of defective modules. Conclusions: The induced rules are simple to use and easy to understand as they provide a description rather than a complete classification of the whole dataset. Thus this paper represents an engineering approach to defect prediction, i.e., an approach which is useful in practice, easily understandable and can be applied by practitioners.
publishDate 2013
dc.date.none.fl_str_mv 2013
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/submittedVersion
format article
status_str submittedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11441/43511
https://doi.org/10.1016/j.infsof.2013.05.002
url http://hdl.handle.net/11441/43511
https://doi.org/10.1016/j.infsof.2013.05.002
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Information and Software Technology, 55 (10), 1810-1822.
IAPP-2012-324356
TIN2011-28956-C02-00
http://dx.doi.org/10.1016/j.infsof.2013.05.002
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.300719

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