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Software Reliability Prediction Using Ensemble Learning on Selected Features in Imbalanced and Balanced Datasets: A Review

Suneel Kumar Rath1, Madhusmita Sahu1, Shom Prasad Das2, Junali Jasmine Jena3, Chitralekha Jena4, Baseem Khan5,6,7,*, Ahmed Ali7, Pitshou Bokoro7

1 Department of Computer Science and Engineering, C.V. Raman Global University, Bhubaneswar, 752054, India
2 Department of Computer Science and Engineering, Birla Global University, Bhubaneswar, 751029, India
3 School of Computer Engineering, KIIT (Deemed to be) University, Bhubaneswar, 751024, India
4 School of Electrical Engineering, KIIT (Deemed to be) University, Bhubaneswar, 751024, India
5 Department of Electrical and Computer Engineering, Hawassa University, Hawassa, P. O. Box 05, Ethiopia
6 Center for Renewable Energy and Microgrids, Huanjiang Laboratory, Zhejiang University, Shaoxing, 311816, China
7 Department of Electrical and Electronic Engineering Technology, University of Johannesburg, Johannesburg, 2028, South Africa

* Corresponding Author: Baseem Khan. Email: email

Computer Systems Science and Engineering 2024, 48(6), 1513-1536. https://doi.org/10.32604/csse.2024.057067

Abstract

Redundancy, correlation, feature irrelevance, and missing samples are just a few problems that make it difficult to analyze software defect data. Additionally, it might be challenging to maintain an even distribution of data relating to both defective and non-defective software. The latter software class’s data are predominately present in the dataset in the majority of experimental situations. The objective of this review study is to demonstrate the effectiveness of combining ensemble learning and feature selection in improving the performance of defect classification. Besides the successful feature selection approach, a novel variant of the ensemble learning technique is analyzed to address the challenges of feature redundancy and data imbalance, providing robustness in the classification process. To overcome these problems and lessen their impact on the fault classification performance, authors carefully integrate effective feature selection with ensemble learning models. Forward selection demonstrates that a significant area under the receiver operating curve (ROC) can be attributed to only a small subset of features. The Greedy forward selection (GFS) technique outperformed Pearson’s correlation method when evaluating feature selection techniques on the datasets. Ensemble learners, such as random forests (RF) and the proposed average probability ensemble (APE), demonstrate greater resistance to the impact of weak features when compared to weighted support vector machines (W-SVMs) and extreme learning machines (ELM). Furthermore, in the case of the NASA and Java datasets, the enhanced average probability ensemble model, which incorporates the Greedy forward selection technique with the average probability ensemble model, achieved remarkably high accuracy for the area under the ROC. It approached a value of 1.0, indicating exceptional performance. This review emphasizes the importance of meticulously selecting attributes in a software dataset to accurately classify damaged components. In addition, the suggested ensemble learning model successfully addressed the aforementioned problems with software data and produced outstanding classification performance.

Keywords


Cite This Article

APA Style
Rath, S.K., Sahu, M., Das, S.P., Jena, J.J., Jena, C. et al. (2024). Software reliability prediction using ensemble learning on selected features in imbalanced and balanced datasets: A review. Computer Systems Science and Engineering, 48(6), 1513-1536. https://doi.org/10.32604/csse.2024.057067
Vancouver Style
Rath SK, Sahu M, Das SP, Jena JJ, Jena C, Khan B, et al. Software reliability prediction using ensemble learning on selected features in imbalanced and balanced datasets: A review. Comput Syst Sci Eng. 2024;48(6):1513-1536 https://doi.org/10.32604/csse.2024.057067
IEEE Style
S.K. Rath et al., “Software Reliability Prediction Using Ensemble Learning on Selected Features in Imbalanced and Balanced Datasets: A Review,” Comput. Syst. Sci. Eng., vol. 48, no. 6, pp. 1513-1536, 2024. https://doi.org/10.32604/csse.2024.057067



cc Copyright © 2024 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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