Stochastic Automata Network of Modeling Parallel Systems
IEEE Transactions on Software Engineering
A Markov Chain Model for Statistical Software Testing
IEEE Transactions on Software Engineering
Efficient descriptor-vector multiplications in stochastic automata networks
Journal of the ACM (JACM)
Generating transition probabilities to support model-based software testing
Software—Practice & Experience
Measuring and Modeling Usage and Reliability for Statistical Web Testing
IEEE Transactions on Software Engineering - Special section on the seventh international software metrics symposium
Probability and Statistics with Reliability, Queuing and Computer Science Applications
Probability and Statistics with Reliability, Queuing and Computer Science Applications
Software Reliability Engineered Testing
Software Reliability Engineered Testing
Quantifying the reliability of software: statistical testing based on a usage model
ISESS '95 Proceedings of the 2nd IEEE Software Engineering Standards Symposium
Improved techniques for software testing based on markov chain usage models
Improved techniques for software testing based on markov chain usage models
Testing software modelling tools using data mutation
Proceedings of the 2006 international workshop on Automation of software test
Performance Models For Master/Slave Parallel Programs
Electronic Notes in Theoretical Computer Science (ENTCS)
Electronic Notes in Theoretical Computer Science (ENTCS)
Performance Evaluation of Software Development Teams: a Practical Case Study
Electronic Notes in Theoretical Computer Science (ENTCS)
A Structured Stochastic Model for Prediction of Geological Stratal Stacking Patterns
Electronic Notes in Theoretical Computer Science (ENTCS)
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The benefits of usage models in the statistical testing of software have been recognized by the software engineering community and discrete state formalisms have been largely used to describe such usage models of software. The Markov Chains formalism (MC) is a natural choice to do so. However, in this paper we suggest the use of a more sophisticated discrete state formalism, the Stochastic Automata Networks (SAN). In many problems of the performance evaluation area, the SAN models present advantages over MC models. Therefore, it seems natural to us to verify similar advantages in the modeling of usage models.This paper presents a case study of building a software usage model using SAN formalism. A software tool called DOCSEDITOR is modeled using MC and SAN. The models are compared in terms of number of states, scalability, and readability. It is not the objective of this paper to present a full framework to develop and analyse usage models with SAN, but just to show some evident advantages of the use of SAN instead of MC. In order to do this, the conclusion points out the indexes than can be computed from both models and suggests the next steps on future work.