Software reliability analysis models
IBM Journal of Research and Development
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
Software cost estimation with fuzzy models
ACM SIGAPP Applied Computing Review
Analysis of a Software Reliability Growth Model with Logistic Testing-Effort Function
ISSRE '97 Proceedings of the Eighth International Symposium on Software Reliability Engineering
Neural Networks, Fuzzy Logic and Genetic Algorithms
Neural Networks, Fuzzy Logic and Genetic Algorithms
Software Reliability Growth Modeling: Models and Applications
IEEE Transactions on Software Engineering
Software reliability prediction by soft computing techniques
Journal of Systems and Software
Journal of Systems and Software
A generalized modified Weibull distribution for lifetime modeling
Computational Statistics & Data Analysis
Fuzzy Reliability Model for Component-Based Software Systems
SEAA '10 Proceedings of the 2010 36th EUROMICRO Conference on Software Engineering and Advanced Applications
Software reusability assessment using soft computing techniques
ACM SIGSOFT Software Engineering Notes
Analogy-based software effort estimation using Fuzzy numbers
Journal of Systems and Software
Fuzzy Emotional COCOMO II Software Cost Estimation (FECSCE) using Multi-Agent Systems
Applied Soft Computing
Reliability analysis and optimal version-updating for open source software
Information and Software Technology
A rule-based approach for estimating the reliability of component-based systems
Advances in Engineering Software
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Context: In this study, a software optimal release time with cost-reliability criteria has been discussed in an imperfect debugging environment. Objective: The motive of this study is to model uncertainty involved in estimated parameters of the software reliability growth model (SRGM). Method: Initially the reliability parameters of SRGM are estimated using least square estimation (LSE). Considering the uncertainty involved in the estimated parameters due to human behavior being subjective in nature and the dynamism of the testing environment, the concept of fuzzy set theory is applicable in developing SRGM. Finally, using arithmetic operations on fuzzy numbers, the reliability and total software cost are calculated. Results: Various reliability measures have been computed at different levels of uncertainties, and a comparison has been made with the existing results reported in the literature. Conclusion: It is evident from the results that a better prediction of reliability measures, namely, software reliability and total software cost can be made under the fuzzy paradigm.