A Methodology for Architecture-Level Reliability Risk Analysis
IEEE Transactions on Software Engineering
Software Component Reliability Analysis
ASSET '99 Proceedings of the 1999 IEEE Symposium on Application - Specific Systems and Software Engineering and Technology
Reliability Simulation of Component-based Software Systems
ISSRE '98 Proceedings of the The Ninth International Symposium on Software Reliability Engineering
A Bayesian Approach to Reliability Prediction and Assessment of Component Based Systems
ISSRE '01 Proceedings of the 12th International Symposium on Software Reliability Engineering
An analytical approach to architecture-based software performance and reliability prediction
Performance Evaluation
Reliability Analysis of Component-Based Software Based on Rewrite Logic
FTDCS '08 Proceedings of the 2008 12th IEEE International Workshop on Future Trends of Distributed Computing Systems
Dependency analysis for component-based software systems
ACM SIGSOFT Software Engineering Notes
Modeling optimal release policy under fuzzy paradigm in imperfect debugging environment
Information and Software Technology
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Reliability is one of the most important nonfunctional requirements for software. Accurately estimating reliability for component-based software systems (CBSSs) is not an easy task, and researchers have proposed many approaches to CBSS reliability estimation. Some of these approaches focus on component reliability and others focus on glue code reliability. All of the approaches that have been proposed are mathematical. However, because reliability is a real-world phenomenon with associated real-time issues, it cannot be measured accurately and efficiently with mathematical models. Soft computing techniques that have recently emerged can be used to model the solution of real-world problems that are too difficult to model mathematically. The two basic soft computing techniques are fuzzy computing and probabilistic computing. In this paper, we focus on four factors that have the strongest effect on CBSS reliability. Based on these four factors, we propose a new fuzzy-logic-based model for estimating CBSS reliability. We implemented and validated our proposed model on small applications, and the results confirm the effectiveness of our model.