research-article

Using SMOTE and Heterogeneous Stacking in Ensemble learning for Software Defect Prediction

Authors:

Sara Adel El-Shorbagy,

Wael Mohamed El-Gammal,

Walid M. AbdelmoezAuthors Info & Claims

ICSIE '18: Proceedings of the 7th International Conference on Software and Information Engineering

Pages 44 - 47

https://doi.org/10.1145/3220267.3220286

Published: 02 May 2018 Publication History

Abstract

Nowadays, there are a lot of classifications models used for predictions in the software engineering field such as effort estimation and defect prediction. One of these models is the ensemble learning machine that improves model performance by combining multiple models in different ways to get a more powerful model.

One of the problems facing the prediction model is the misclassification of the minority samples. This problem mainly appears in the case of defect prediction. Our aim is the classification of defects which are considered minority samples during the training phase. This can be improved by implementing the Synthetic Minority Over-Sampling Technique (SMOTE) before the implementation of the ensemble model which leads to over-sample the minority class instances.

In this paper, our work propose applying a new ensemble model by combining the SMOTE technique with the heterogeneous stacking ensemble to get the most benefit and performance in training a dataset that focus on the minority subset as in the software prediction study. Our proposed model shows better performance that overcomes other techniques results applied on the minority samples of the defect prediction.

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Cited By

Chen JXu JCai SWang XChen HLi Z(2024)Software Defect Prediction Approach Based on a Diversity Ensemble Combined With Neural NetworkIEEE Transactions on Reliability10.1109/TR.2024.335651573:3(1487-1501)Online publication date: Sep-2024
https://doi.org/10.1109/TR.2024.3356515
Al-Fraihat DSharrab YAl-Ghuwairi AAlshishani HAlgarni A(2024)Hyperparameter Optimization for Software Bug Prediction Using Ensemble LearningIEEE Access10.1109/ACCESS.2024.338002412(51869-51878)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3380024
Hernández-Molinos MSánchez-García ABarrientos-Martínez RPérez-Arriaga JOcharán-Hernández J(2023)Software Defect Prediction with Bayesian ApproachesMathematics10.3390/math1111252411:11(2524)Online publication date: 31-May-2023
https://doi.org/10.3390/math11112524
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Index Terms

Using SMOTE and Heterogeneous Stacking in Ensemble learning for Software Defect Prediction
1. Software and its engineering
  1. Software creation and management
    1. Software verification and validation

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ICSIE '18: Proceedings of the 7th International Conference on Software and Information Engineering

May 2018

147 pages

ISBN:9781450364690

DOI:10.1145/3220267

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 02 May 2018

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ICSIE '18

ICSIE '18: 7th International Conference on Software and Information Engineering

May 2 - 4, 2018

Cairo, Egypt

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Cited By

Chen JXu JCai SWang XChen HLi Z(2024)Software Defect Prediction Approach Based on a Diversity Ensemble Combined With Neural NetworkIEEE Transactions on Reliability10.1109/TR.2024.335651573:3(1487-1501)Online publication date: Sep-2024
https://doi.org/10.1109/TR.2024.3356515
Al-Fraihat DSharrab YAl-Ghuwairi AAlshishani HAlgarni A(2024)Hyperparameter Optimization for Software Bug Prediction Using Ensemble LearningIEEE Access10.1109/ACCESS.2024.338002412(51869-51878)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3380024
Hernández-Molinos MSánchez-García ABarrientos-Martínez RPérez-Arriaga JOcharán-Hernández J(2023)Software Defect Prediction with Bayesian ApproachesMathematics10.3390/math1111252411:11(2524)Online publication date: 31-May-2023
https://doi.org/10.3390/math11112524
Dey ADas S(2023)Model for Predicting Lateral Shifts and Driving Style of Motorized Two-WheelersTransportation Research Record: Journal of the Transportation Research Board10.1177/036119812312032202678:6(972-988)Online publication date: 31-Oct-2023
https://doi.org/10.1177/03611981231203220
Olaleye TArogundade OMisra SAbayomi-Alli AKose U(2023)Predictive Analytics and Software Defect Severity: A Systematic Review and Future DirectionsScientific Programming10.1155/2023/62213882023(1-18)Online publication date: 2-Feb-2023
https://doi.org/10.1155/2023/6221388
Wei CXiao LYu TChen XWang XWong SClune A(2023)Automatically Tagging the “AAA” Pattern in Unit Test Cases Using Machine Learning ModelsIEEE Transactions on Software Engineering10.1109/TSE.2023.325244249:5(3305-3324)Online publication date: 1-May-2023
https://doi.org/10.1109/TSE.2023.3252442
Olivares-Galindo JSánchez-García ÁBarrientos-Martínez ROcharán-Hernández J(2023)Ensemble Classifiers in Software Defect Prediction: A Systematic Literature Review2023 11th International Conference in Software Engineering Research and Innovation (CONISOFT)10.1109/CONISOFT58849.2023.00011(1-8)Online publication date: 6-Nov-2023
https://doi.org/10.1109/CONISOFT58849.2023.00011
Saravanan PSangeetha V(2022)African buffalo optimized multinomial softmax regression based convolutional deep neural network for software fault predictionMaterials Today: Proceedings10.1016/j.matpr.2021.08.09761(619-626)Online publication date: 2022
https://doi.org/10.1016/j.matpr.2021.08.097
Bhandari KKumar KSangal A(2022)Data quality issues in software fault prediction: a systematic literature reviewArtificial Intelligence Review10.1007/s10462-022-10371-656:8(7839-7908)Online publication date: 21-Dec-2022
https://doi.org/10.1007/s10462-022-10371-6
Balogun AOdejide BBajeh AAlanamu ZUsman-Hamza FAdeleke HMabayoje MYusuff S(2022)Empirical Analysis of Data Sampling-Based Ensemble Methods in Software Defect PredictionComputational Science and Its Applications – ICCSA 2022 Workshops10.1007/978-3-031-10548-7_27(363-379)Online publication date: 26-Jul-2022
https://doi.org/10.1007/978-3-031-10548-7_27
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