Deep-Learning Software Reliability Model Using SRGM as Activation Function
-
Published:2023-09-29
Issue:19
Volume:13
Page:10836
-
ISSN:2076-3417
-
Container-title:Applied Sciences
-
language:en
-
Short-container-title:Applied Sciences
Author:
Kim Youn Su1, Pham Hoang2, Chang In Hong1
Affiliation:
1. Department of Computer Science and Statistics, Chosun University, 309 Pilmun-daero, Dong-gu, Gwangju 61452, Republic of Korea 2. Department of Industrial and Systems Engineering, Rutgers University, 96 Frelinghuysen Road, Piscataway, NJ 08855-8018, USA
Abstract
Software is widely used in various fields. There is no place where it is not used from the smallest part to the entire part. In particular, the tendency to rely on software is accelerating as the fields of artificial intelligence and big data become more widespread. Therefore, it is extremely important to evaluate the reliability of software because of the extensive damage that could occur if the software fails. Previously, software reliability models were developed based on mathematical and statistical grounds; however, immediate response was difficult. Therefore, in this study, a software reliability model was developed that depends on data using deep learning, and it was analyzed by replacing the activation function previously used in deep learning with the proposed software reliability model. Since the sigmoid function has a similar shape to the software reliability model, we utilized this to propose a deep learning software reliability model that replaces the activation function, the sigmoid function, with the software reliability function. Two datasets were compared and analyzed using 10 criteria, and the superiority of the proposed deep-learning software reliability model was proved. In addition, the results were compared by changing the parameters utilized in the proposed deep-learning software reliability model by −10%, −5%, 5%, and 10%, and it was found that the larger the parameters, the smaller the change.
Funder
Basic Science Research Program of the National Research Foundation of Korea
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference67 articles.
1. Statistical perspectives on reliability of artificial intelligence systems;Hong;Qual. Eng.,2023 2. Assessment of the reliability of AI programs;Bastani;Int. J. Artif. Intell. Tools,1993 3. Sheptunov, S.A., Larionov, M.V., Suhanova, N.V., Il’ya, S.K., and Alshynbaeva, D.A. (2016, January 4–11). Optimization of the complex software reliability of control systems. Proceedings of the 2016 IEEE Conference on Quality Management, Transport and Information Security, Information Technologies (IT&MQ&IS), Nalchik, Russia. 4. In AI we trust: Ethics, artificial intelligence, and reliability;Ryan;Sci. Eng. Ethics,2020 5. Martyushev, N.V.L.L., Malozyomov, B.V., Sorokova, S.N., Efremenkov, E.A., Valuev, D.V., and Qi, M. (2023). Review Models and Methods for Determining and Predicting the Reliability of Technical Systems and Transport. Mathematics, 11.
|
|