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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. [ABSTRACT FROM AUTHOR]

Service-oriented architecture (SOA) has become an increasingly popular choice for building software application in the last years. An SOA system is an elastic structure that utilizes services discovery and integrates these services to perform specified functions. In general, reliability is a critical system attribute when evaluating the quality of a well-built software applications. But it has to be noted that the phenomenon of error propagation could have significant impacts on system reliability. Propagated errors may be masked or propagated to the system interface, which can thereby lead to a system failure. Much research on reliability evaluation for SOA systems have been proposed in the past. However, most of these studies have neglected the phenomenon of error propagation and the issue of link failure. In this article, we take a different view of error propagation, fault tolerance, and the failure behavior of links between services and try to develop an enhanced SOA reliability prediction model incorporating error propagation and fault tolerance (EP–FT). Different fault tolerance techniques will be selected and integrated into the SOA systems. Additionally, sensitivity analysis is also presented and discussed to determine the critical services in the system. Experiments are performed based on four real-world case studies. We will show and discuss the experimental results of the proposed EP–FT reliability model and simulation-based approach in detail. Our experimental results show that the impact of error propagation on system reliability is not negligible, and the SOA systems with fault tolerance demonstrate higher reliability than those that do not. [ABSTRACT FROM AUTHOR]