An enhanced conventional neural network schema for structural class-based software fault prediction
Author:
Affiliation:
1. Department of Computer Science, Ilma University, Karachi, Pakistan
2. School of Business, University of Southern Queensland, Springfield, Australia
Publisher
Informa UK Limited
Link
https://www.tandfonline.com/doi/pdf/10.1080/23742917.2024.2402014
Reference30 articles.
1. Steidl D, Deissenboeck F, Poehlmann M, et al. Continuous software quality control in practice. In: Software Maintenance and Evolution (ICSME), 2014 IEEE International Conference; California, USA. 2014 Sep; IEEE. p. 561–564.
2. A Review of Software Quality Models for the Evaluation of Software Products
3. Software defect prediction using ensemble learning on selected features
4. Software fault prediction: a review;Kumar R;Info Sec Jour,2015
5. Bacchelli MD, Lanza M. Are popular classes more defect-prone? In: Proceedings of the 13th Interna- tional Conference on Fundamental Approaches to Software Engineering FASE’; 2010. Vol. 10; Berlin, Heidelberg. Springer-Verlag. p. 59–73.
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