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Nonlinear Geometric Framework for Software Defect Prediction

Nonlinear Geometric Framework for Software Defect Prediction

Misha Kakkar, Sarika Jain, Abhay Bansal, P. S. Grover
Copyright: © 2020 |Volume: 12 |Issue: 3 |Pages: 16
ISSN: 1941-6296|EISSN: 1941-630X|EISBN13: 9781799805991|DOI: 10.4018/IJDSST.2020070105
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MLA

Kakkar, Misha, et al. "Nonlinear Geometric Framework for Software Defect Prediction." IJDSST vol.12, no.3 2020: pp.85-100. http://doi.org/10.4018/IJDSST.2020070105

APA

Kakkar, M., Jain, S., Bansal, A., & Grover, P. S. (2020). Nonlinear Geometric Framework for Software Defect Prediction. International Journal of Decision Support System Technology (IJDSST), 12(3), 85-100. http://doi.org/10.4018/IJDSST.2020070105

Chicago

Kakkar, Misha, et al. "Nonlinear Geometric Framework for Software Defect Prediction," International Journal of Decision Support System Technology (IJDSST) 12, no.3: 85-100. http://doi.org/10.4018/IJDSST.2020070105

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Abstract

Humans use the software in every walk of life thus it is essential to have the best quality software. Software defect prediction models assist in identifying defect prone modules with the help of historical data, which in turn improves software quality. Historical data consists of data related to modules /files/classes which are labeled as buggy or clean. As the number of buggy artifacts as less as compared to clean artifacts, the nature of historical data becomes imbalance. Due to this uneven distribution of the data, it difficult for classification algorithms to build highly effective SDP models. The objective of this study is to propose a new nonlinear geometric framework based on SMOTE and ensemble learning to improve the performance of SDP models. The study combines the traditional SMOTE algorithm and the novel ensemble Support Vector Machine (SVM) is used to develop the proposed framework called SMEnsemble. SMOTE algorithm handles the class imbalance problem by generating synthetic instances of the minority class. Ensemble learning generates multiple classification models to select the best performing SDP model. For experimentation, datasets from three different software repositories that contain both open source as well as proprietary projects are used in the study. The results show that SMEnsemble performs better than traditional methods for identifying the minority class i.e. buggy artifacts. Also, the proposed model performance is better than the latest state of Art SDP model- SMOTUNED. The proposed model is capable of handling imbalance classes when compared with traditional methods. Also, by carefully selecting the number of ensembles high performance can be achieved in less time.

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