Software Defect Prediction Using Wrapper Feature Selection Based on Dynamic Re-Ranking Strategy

Finding defects early in a software system is a crucial task, as it creates adequate time for fixing such defects using available resources. Strategies such as symmetric testing have proven useful; however, its inability in differentiating incorrect implementations from correct ones is a drawback. S...

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Autores principales:	Abdullateef Oluwagbemiga Balogun, Shuib Basri, Luiz Fernando Capretz, Saipunidzam Mahamad, Abdullahi Abubakar Imam, Malek A. Almomani, Victor Elijah Adeyemo, Ammar K. Alazzawi, Amos Orenyi Bajeh, Ganesh Kumar
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	high dimensionality re-ranking strategy software defect prediction wrapper feature method Cuckoo search method Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/ff406c16769e473cba37a8ef07580c9b
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spelling	oai:doaj.org-article:ff406c16769e473cba37a8ef07580c9b2021-11-25T19:07:16ZSoftware Defect Prediction Using Wrapper Feature Selection Based on Dynamic Re-Ranking Strategy10.3390/sym131121662073-8994https://doaj.org/article/ff406c16769e473cba37a8ef07580c9b2021-11-01T00:00:00Zhttps://www.mdpi.com/2073-8994/13/11/2166https://doaj.org/toc/2073-8994Finding defects early in a software system is a crucial task, as it creates adequate time for fixing such defects using available resources. Strategies such as symmetric testing have proven useful; however, its inability in differentiating incorrect implementations from correct ones is a drawback. Software defect prediction (SDP) is another feasible method that can be used for detecting defects early. Additionally, high dimensionality, a data quality problem, has a detrimental effect on the predictive capability of SDP models. Feature selection (FS) has been used as a feasible solution for solving the high dimensionality issue in SDP. According to current literature, the two basic forms of FS approaches are filter-based feature selection (FFS) and wrapper-based feature selection (WFS). Between the two, WFS approaches have been deemed to be superior. However, WFS methods have a high computational cost due to the unknown number of executions available for feature subset search, evaluation, and selection. This characteristic of WFS often leads to overfitting of classifier models due to its easy trapping in local maxima. The trapping of the WFS subset evaluator in local maxima can be overcome by using an effective search method in the evaluator process. Hence, this study proposes an enhanced WFS method that dynamically and iteratively selects features. The proposed enhanced WFS (EWFS) method is based on incrementally selecting features while considering previously selected features in its search space. The novelty of EWFS is based on the enhancement of the subset evaluation process of WFS methods by deploying a dynamic re-ranking strategy that iteratively selects germane features with a low subset evaluation cycle while not compromising the prediction performance of the ensuing model. For evaluation, EWFS was deployed with Decision Tree (DT) and Naïve Bayes classifiers on software defect datasets with varying granularities. The experimental findings revealed that EWFS outperformed existing metaheuristics and sequential search-based WFS approaches established in this work. Additionally, EWFS selected fewer features with less computational time as compared with existing metaheuristics and sequential search-based WFS methods.Abdullateef Oluwagbemiga BalogunShuib BasriLuiz Fernando CapretzSaipunidzam MahamadAbdullahi Abubakar ImamMalek A. AlmomaniVictor Elijah AdeyemoAmmar K. AlazzawiAmos Orenyi BajehGanesh KumarMDPI AGarticlehigh dimensionalityre-ranking strategysoftware defect predictionwrapper feature methodCuckoo search methodMathematicsQA1-939ENSymmetry, Vol 13, Iss 2166, p 2166 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	high dimensionality re-ranking strategy software defect prediction wrapper feature method Cuckoo search method Mathematics QA1-939
spellingShingle	high dimensionality re-ranking strategy software defect prediction wrapper feature method Cuckoo search method Mathematics QA1-939 Abdullateef Oluwagbemiga Balogun Shuib Basri Luiz Fernando Capretz Saipunidzam Mahamad Abdullahi Abubakar Imam Malek A. Almomani Victor Elijah Adeyemo Ammar K. Alazzawi Amos Orenyi Bajeh Ganesh Kumar Software Defect Prediction Using Wrapper Feature Selection Based on Dynamic Re-Ranking Strategy
description	Finding defects early in a software system is a crucial task, as it creates adequate time for fixing such defects using available resources. Strategies such as symmetric testing have proven useful; however, its inability in differentiating incorrect implementations from correct ones is a drawback. Software defect prediction (SDP) is another feasible method that can be used for detecting defects early. Additionally, high dimensionality, a data quality problem, has a detrimental effect on the predictive capability of SDP models. Feature selection (FS) has been used as a feasible solution for solving the high dimensionality issue in SDP. According to current literature, the two basic forms of FS approaches are filter-based feature selection (FFS) and wrapper-based feature selection (WFS). Between the two, WFS approaches have been deemed to be superior. However, WFS methods have a high computational cost due to the unknown number of executions available for feature subset search, evaluation, and selection. This characteristic of WFS often leads to overfitting of classifier models due to its easy trapping in local maxima. The trapping of the WFS subset evaluator in local maxima can be overcome by using an effective search method in the evaluator process. Hence, this study proposes an enhanced WFS method that dynamically and iteratively selects features. The proposed enhanced WFS (EWFS) method is based on incrementally selecting features while considering previously selected features in its search space. The novelty of EWFS is based on the enhancement of the subset evaluation process of WFS methods by deploying a dynamic re-ranking strategy that iteratively selects germane features with a low subset evaluation cycle while not compromising the prediction performance of the ensuing model. For evaluation, EWFS was deployed with Decision Tree (DT) and Naïve Bayes classifiers on software defect datasets with varying granularities. The experimental findings revealed that EWFS outperformed existing metaheuristics and sequential search-based WFS approaches established in this work. Additionally, EWFS selected fewer features with less computational time as compared with existing metaheuristics and sequential search-based WFS methods.
format	article
author	Abdullateef Oluwagbemiga Balogun Shuib Basri Luiz Fernando Capretz Saipunidzam Mahamad Abdullahi Abubakar Imam Malek A. Almomani Victor Elijah Adeyemo Ammar K. Alazzawi Amos Orenyi Bajeh Ganesh Kumar
author_facet	Abdullateef Oluwagbemiga Balogun Shuib Basri Luiz Fernando Capretz Saipunidzam Mahamad Abdullahi Abubakar Imam Malek A. Almomani Victor Elijah Adeyemo Ammar K. Alazzawi Amos Orenyi Bajeh Ganesh Kumar
author_sort	Abdullateef Oluwagbemiga Balogun
title	Software Defect Prediction Using Wrapper Feature Selection Based on Dynamic Re-Ranking Strategy
title_short	Software Defect Prediction Using Wrapper Feature Selection Based on Dynamic Re-Ranking Strategy
title_full	Software Defect Prediction Using Wrapper Feature Selection Based on Dynamic Re-Ranking Strategy
title_fullStr	Software Defect Prediction Using Wrapper Feature Selection Based on Dynamic Re-Ranking Strategy
title_full_unstemmed	Software Defect Prediction Using Wrapper Feature Selection Based on Dynamic Re-Ranking Strategy
title_sort	software defect prediction using wrapper feature selection based on dynamic re-ranking strategy
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/ff406c16769e473cba37a8ef07580c9b
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Software Defect Prediction Using Wrapper Feature Selection Based on Dynamic Re-Ranking Strategy

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