Measuring Software Reliability: A Trend Using Machine Learning Techniques-Reference-Cited by-同舟云学术

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Measuring Software Reliability: A Trend Using Machine Learning Techniques

Published:2014-11-30 Issue: Volume: Page:807-829
ISSN:1434-9922
Container-title:Complex System Modelling and Control Through Intelligent Soft Computations
language:
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Author:

Kumar Nishikant,Banerjee Soumya

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-319-12883-2_28

Reference33 articles.

1. Aggarwal, K., Singh, Y., Kaur, A., & Malhotra, R. (2009a). Empirical analysis for investigating the effect of object-oriented metrics on fault proneness: A replicated case study. Software Process: Improvement and Practice, 16(1), 39–62.

2. Aggarwal, K., Singh, Y., Kaur, A., & Malhotra, R. (2009b). Empirical analysis for investigating the effect of object-oriented metrics on fault proneness: A replicated case study. Software Process: Improvement and Practice, 16(1), 39–62.

3. Barker, M., & Rayens, W. (2003). Partial least squares for discrimination. Journal of Chemometrics, 17(3), 166–173.

4. Bird, C., Ranganath, V.-P., Zimmermann, T., Nagappan, N., & Zeller, A. (2014s). Extrinsic influence factors in software reliability: A study of 200,000 windows machines. In Proceedings of the 36th International Conference on Software Engineering (ICSE 2014). Hyderabad, India: ACM.

5. Chidamber, S. R., & Kemerer, C. F. (1994). Metrics suite for object oriented design. IEEE Transactions on Software Engineering, 20(6), 476–493.

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1. A new boosting-based software reliability growth model;Communications in Statistics - Theory and Methods;2020-03-18

2. A robust weighted SVR-based software reliability growth model;Reliability Engineering & System Safety;2018-08

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