A robust weighted SVR-based software reliability growth model
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DOI: 10.1016/j.ress.2018.04.007
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References listed on IDEAS
- Hu, Q.P. & Xie, M. & Ng, S.H. & Levitin, G., 2007. "Robust recurrent neural network modeling for software fault detection and correction prediction," Reliability Engineering and System Safety, Elsevier, vol. 92(3), pages 332-340.
- Yang, Bo & Li, Xiang & Xie, Min & Tan, Feng, 2010. "A generic data-driven software reliability model with model mining technique," Reliability Engineering and System Safety, Elsevier, vol. 95(6), pages 671-678.
- Chiu, Kuei-Chen & Huang, Yeu-Shiang & Lee, Tzai-Zang, 2008. "A study of software reliability growth from the perspective of learning effects," Reliability Engineering and System Safety, Elsevier, vol. 93(10), pages 1410-1421.
- Moura, Márcio das Chagas & Zio, Enrico & Lins, Isis Didier & Droguett, Enrique, 2011. "Failure and reliability prediction by support vector machines regression of time series data," Reliability Engineering and System Safety, Elsevier, vol. 96(11), pages 1527-1534.
- Hoang Pham, 2006. "System Software Reliability," Springer Series in Reliability Engineering, Springer, number 978-1-84628-295-9, March.
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Cited by:
- Yuan, Xiukai & Faes, Matthias G.R. & Liu, Shaolong & Valdebenito, Marcos A. & Beer, Michael, 2021. "Efficient imprecise reliability analysis using the Augmented Space Integral," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
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Keywords
Imprecise contaminated model; Pairwise comparisons; Quadratic programming; Software reliability growth model; Support vector regression;All these keywords.
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