Title: Software reliability testing coverage model using feed-forward back propagation neural network

Authors: Ritu Bibyan; Sameer Anand; Ajay Jaiswal; Anu Gupta Aggarwal

Addresses: Department of Operational Research, University of Delhi, Delhi, 110007, India ' Shaheed Sukhdev College of Business Studies, University of Delhi, Delhi, 110089, India ' College of Vocational Studies, University of Delhi, Delhi, 110017, India ' Department of Operational Research, University of Delhi, New-Delhi, 110007, India

Abstract: The paper presents software reliability growth model (SRGM) with testing coverage which covers both detection as well as correction process under imperfect debugging. The estimation is done using feed forward back propagation artificial neural network (ANN). Many researchers have studied the importance of modelling fault detection instead of modelling fault correction. We have proposed generalised testing coverage model by adopting different testing coverage for both the processes. We have also compared proposed model with existing traditional models based on three failure datasets. Different performance criteria like goodness of fit, accuracy of the model, mean square error (MSE), and coefficient of determination (R²) are evaluated for the datasets. The comparison results shows that the model proposed in this paper provides more efficient accuracy than the existing traditional models.

Keywords: software reliability; testing coverage; machine learning; feed-forward; back-propagation; neural network.

DOI: 10.1504/IJMIC.2023.132609

International Journal of Modelling, Identification and Control, 2023 Vol.43 No.2, pp.126 - 133

Received: 11 May 2022
Received in revised form: 29 Jul 2022
Accepted: 28 Sep 2022
Published online: 30 Jul 2023 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article