A Cloud-Based Software Defect Prediction System Using Data and Decision-Level Machine Learning Fusion

Shabib Aftab, Sagheer Abbas, Taher M. Ghazal, Munir Ahmad, Hussam Al Hamadi, Chan Yeob Yeun () and Muhammad Adnan Khan ()
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Shabib Aftab: School of Computer Science, National College of Business Administration and Economics, Lahore 54000, Pakistan
Sagheer Abbas: School of Computer Science, National College of Business Administration and Economics, Lahore 54000, Pakistan
Taher M. Ghazal: Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Selangor, Malaysia
Munir Ahmad: School of Computer Science, National College of Business Administration and Economics, Lahore 54000, Pakistan
Hussam Al Hamadi: College of Engineering and IT, University of Dubai, Dubai 14143, United Arab Emirates
Chan Yeob Yeun: Center for Cyber Physical Systems, EECS Dept, Khalifa University, Abu Dhabi 127788, United Arab Emirates
Muhammad Adnan Khan: Department of Software, Faculty of Artificial Intelligence and Software, Gachon University, Seongnam 13120, Republic of Korea

Mathematics, 2023, vol. 11, issue 3, 1-15

Abstract: This research contributes an intelligent cloud-based software defect prediction system using data and decision-level machine learning fusion techniques. The proposed system detects the defective modules using a two-step prediction method. In the first step, the prediction is performed using three supervised machine learning techniques, including naïve Bayes, artificial neural network, and decision tree. These classification techniques are iteratively tuned until the maximum accuracy is achieved. In the second step, the final prediction is performed by fusing the accuracy of the used classifiers with a fuzzy logic-based system. The proposed fuzzy logic technique integrates the predictive accuracy of the used classifiers using eight if–then fuzzy rules in order to achieve a higher performance. In the study, to implement the proposed fusion-based defect prediction system, five datasets were fused, which were collected from the NASA repository, including CM1, MW1, PC1, PC3, and PC4. It was observed that the proposed intelligent system achieved a 91.05% accuracy for the fused dataset and outperformed other defect prediction techniques, including base classifiers and state-of-the-art ensemble techniques.

Keywords: machine learning; software defect prediction; data fusion; machine learning fusion; fuzzy system (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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