Year: 2024
Type: article
Abstract: Software reliability is a crucial factor in determining software quality quantitatively. It is also used to estimate the software testing duration. In software reliability testing, traditional parametric software reliability growth models (SRGMs) are effectively used. Nevertheless, a single parametric model cannot provide accurate predictions in all cases. Moreover, non-parametric models have proven to be efficient for predicting software reliability as alternatives to parametric models. In this paper, we adopted a deep learning method for software reliability testing in computer vision systems. Also, we focused on critical computer vision applications that need high reliability. We propose a new deep learning-based model that is combined and based on the ensemble method to improve the performance of software reliability testing. The experimental results of the new model architecture present fairly accurate predictive capability compared to other existing single Neural Network (NN) based models.
Source: Computers
Institutions: Taif University, King Abdulaziz University
Language: English
Subfield: Software
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,046
Funder: King Abdulaziz University
OpenAlex ID: W4405366732