Estimating the Probability of Failure When Testing Reveals No Failures
IEEE Transactions on Software Engineering
A maximum entropy approach to natural language processing
Computational Linguistics
Total Variance Approach to Software Reliability Estimation
IEEE Transactions on Software Engineering - Special issue: best papers of the sixth international workshop on Petri nets and performance models (PNPM'95)
Software Reliability
Probability and Statistics with Reliability, Queuing and Computer Science Applications
Probability and Statistics with Reliability, Queuing and Computer Science Applications
Bayesian Graphical Models for Software Testing
IEEE Transactions on Software Engineering
Confidence Interval Estimation of NHPP-Based Software Reliability Models
ISSRE '99 Proceedings of the 10th International Symposium on Software Reliability Engineering
Optimal testing-resource allocation with genetic algorithm for modular software systems
Journal of Systems and Software
Systematic Reliability Analysis of a Class of Application-Specific Embedded Software Frameworks
IEEE Transactions on Software Engineering
Computing System Reliability: Models And Analysis
Computing System Reliability: Models And Analysis
Reliability and Validity in Comparative Studies of Software Prediction Models
IEEE Transactions on Software Engineering
Enabling Reuse-Based Software Development of Large-Scale Systems
IEEE Transactions on Software Engineering
Bayesian Extensions to a Basic Model of Software Reliability
IEEE Transactions on Software Engineering
Software defect detection with rocus
Journal of Computer Science and Technology
Expert Systems with Applications: An International Journal
The conjunctive combination of interval-valued belief structures from dependent sources
International Journal of Approximate Reasoning
Security and Communication Networks
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In software reliability modeling, the parameters of the model are typically estimated from the test data of the corresponding component. However, the widely used point estimators are subject to random variations in the data, resulting in uncertainties in these estimated parameters. For large complex systems made up of many components, the uncertainty of each individual parameter amplifies the uncertainty of the total system reliability. Ignoring the parameter uncertainty can result in grossly underestimating the uncertainty in the total system reliability. This paper attempts to study and quantify the uncertainties in the software reliability modeling of a single component with correlated parameters and in a large system with numerous components. Previous works on quantifying uncertainties have assumed a sufficient amount of available data. However, a characteristic challenge in software testing and reliability is the lack of available failure data from a single test which often makes modeling difficult. This lack of data poses a bigger challenge in the uncertainty analysis of the software reliability modeling. To overcome this challenge, this paper proposes to utilize experts' opinions and historical data from previous projects to complement the small number of observations to quantify the uncertainties. This is done by combining the Maximum-Entropy Principle (MEP) into the Bayesian approach. This paper further considers the uncertainty analysis at the system level which contains multiple components, each with its respective model/parameter/uncertainty using a Monte Carlo approach. Some examples with different modeling approaches (NHPP, Markov, Graph theory) are illustrated to show the generality and effectiveness of the proposed approach. Furthermore, we illustrate how the proposed approach for considering the uncertainties in various components improves a large-scale system reliability model proposed in Dai & Levitin (2006) by relaxing a critical assumption.