A Study of Incorporation of Deep Learning Into Software Reliability Modeling and Assessment

Abstract

Software is widely used in many application domains. The most popular software are used by millions every day. How to accurately predict and assess the reliability of developed software is becoming increasingly important for project managers and developers. Previous studies have primarily used the software reliability growth model (SRGM) to evaluate and predict software reliability, but prediction results cannot be accurate at particular times or in particular situations. One of the main reasons is that simplified assumptions and abstractions are usually made to simplify the problem when developing SRGMs. Selecting an appropriate SRGM should depend on the key characteristics of the software project. In this article, we propose a deep learning-based approach for software reliability prediction and assessment. Specifically, we clearly demonstrate how to derive mathematical expressions from the computational methods of deep learning models and how to determine the correlation between them and the mathematical formula of SRGMs, and then, we use the back-propagation algorithm to obtain the SRGM parameters. Furthermore, we further integrate some deep learning-based SRGMs and also propose a method for the weighted assignment of combinations. Three real open source software failure datasets are used to evaluate the performance of the proposed models compared to selected SRGMs. The experimental results reveal that our proposed deep learning-based models and their combinations perform better than several classical SRGMs.