Software defect prediction model based on improved twin support vector machines-Reference-Cited by-同舟云学术

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Software defect prediction model based on improved twin support vector machines

Published:2023-04-01 Issue:21 Volume:27 Page:16101-16110
ISSN:1432-7643
Container-title:Soft Computing
language:en
Short-container-title:Soft Comput

Author:

Liu Jianming,Lei Jie^ORCID,Liao Zhouyu,He Jiali

Funder

the National Natural Science Foundation of Guangxi

the Guangxi University Young Teachers Foundation Competence Improvement Project

Natural Science Foundation of China

Guangxi Natural Science Foundation

the Natural Science Foundation of Yulin City of China

Publisher

Springer Science and Business Media LLC

Subject

Geometry and Topology,Theoretical Computer Science,Software

Link

https://link.springer.com/content/pdf/10.1007/s00500-023-07984-6.pdf

Reference56 articles.

1. Adak MF (2018) Software defect detection by using data mining based fuzzy logic. In: 2018 Sixth international conference on digital information, networking, and wireless communications (DINWC), pp.65–69. IEEE Press

2. Agarwal S, Tomar D, Verma S (2014) Prediction of software defects using Twin Support Vector Machine. In: 2014 International Conference on Information Systems and Computer Networks (ISCON), pp.128–132. IEEE Press

3. Andreou AS, Chatzis SP (2016) Software defect prediction using doubly stochastic Poisson processes driven by stochastic belief networks. J Syst Softw 122:72–82

4. Chen X, Zhang D, Zhao Y, Cui Z, Ni C (2019) Software defect number prediction: Unsupervised vs supervised methods. Inf Softw Technol 106:161–181

5. Dekhandji FZ (2017) Signal processing deployment in power quality disturbance detection and classification. Acta Phys Pol. Ser. A 132(3):415–419

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1. Handling class overlap and imbalance using overlap driven under-sampling with balanced random forest in software defect prediction;Innovations in Systems and Software Engineering;2024-06-18

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