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...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/13/11/2166 |
| _version_ | 1797508412477538304 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T05:01:44Z |
| format | Article |
| id | doaj.art-ff406c16769e473cba37a8ef07580c9b |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-10T05:01:44Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-ff406c16769e473cba37a8ef07580c9b2023-11-23T01:46:11ZengMDPI AGSymmetry2073-89942021-11-011311216610.3390/sym13112166Software Defect Prediction Using Wrapper Feature Selection Based on Dynamic Re-Ranking StrategyAbdullateef Oluwagbemiga Balogun0Shuib Basri1Luiz Fernando Capretz2Saipunidzam Mahamad3Abdullahi Abubakar Imam4Malek A. Almomani5Victor Elijah Adeyemo6Ammar K. Alazzawi7Amos Orenyi Bajeh8Ganesh Kumar9Department of Computer and Information Science, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, MalaysiaDepartment of Computer and Information Science, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, MalaysiaDepartment of Electrical and Computer Engineering, Western University, London, ON N6A 5B9, CanadaDepartment of Computer and Information Science, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, MalaysiaDepartment of Computer and Information Science, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, MalaysiaDepartment of Software Engineering, The World Islamic Sciences and Education University, Amman 11947, JordanSchool of Built Environment, Engineering and Computing, Headingley Campus, Leeds Beckett University, Leeds LS6 3QS, UKDepartment of Computer and Information Science, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, MalaysiaDepartment of Computer Science, University of Ilorin, Ilorin 1515, NigeriaDepartment of Computer and Information Science, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, MalaysiaFinding 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.https://www.mdpi.com/2073-8994/13/11/2166high dimensionalityre-ranking strategysoftware defect predictionwrapper feature methodCuckoo search method |
| spellingShingle | 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 Symmetry high dimensionality re-ranking strategy software defect prediction wrapper feature method Cuckoo search method |
| title | 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_short | 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 |
| topic | high dimensionality re-ranking strategy software defect prediction wrapper feature method Cuckoo search method |
| url | https://www.mdpi.com/2073-8994/13/11/2166 |
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