Article

Test Case Generation Using Stochastic Automata Networks: Quantitative Analysis

Authors:

Cristiano Bertolini,

Farina Farina,

Paulo Fernandes,

Flavio M. OliveiraAuthors Info & Claims

SEFM '04: Proceedings of the Software Engineering and Formal Methods, Second International Conference

Pages 251 - 260

Published: 28 September 2004 Publication History

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Abstract

The software engineering community has been using Markov Chains (MC) to describe usage models. We have been working on the use of a more sophisticated discrete state formalism: Stochastic Automata Networks (SAN). SAN is a formalism with the same power of description as MC; however, a system in SAN is described as a collection of subsystems described by local states, transitions and synchronizing events, allowing higher modularity and maintainability. We present a description of SAN formalism, as well as quantitative analysis of the modeling examples considering the generation time, quality of the test suites.

Cited By

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Czekster RFernandes PLopes LSales ASantos AWebber T(2016)Stochastic Performance Analysis of Global Software Development TeamsACM Transactions on Software Engineering and Methodology10.1145/295509325:3(1-32)Online publication date: 22-Aug-2016
https://dl.acm.org/doi/10.1145/2955093
Mao YBoqin FLi ZYao L(2006)Neural networks based automated test oracle for software testingProceedings of the 13th international conference on Neural information processing - Volume Part III10.5555/1986861.1986921(498-507)Online publication date: 3-Oct-2006
https://dl.acm.org/doi/10.5555/1986861.1986921
Shan LZhu HZhu HHorgan JCheung SLi J(2006)Testing software modelling tools using data mutationProceedings of the 2006 international workshop on Automation of software test10.1145/1138929.1138938(43-49)Online publication date: 23-May-2006
https://dl.acm.org/doi/10.1145/1138929.1138938

Test Case Generation Using Stochastic Automata Networks: Quantitative Analysis
1. Theory of computation

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SEFM '04: Proceedings of the Software Engineering and Formal Methods, Second International Conference

September 2004

401 pages

ISBN:076952222X

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IEEE Computer Society

United States

Publication History

Published: 28 September 2004

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Czekster RFernandes PLopes LSales ASantos AWebber T(2016)Stochastic Performance Analysis of Global Software Development TeamsACM Transactions on Software Engineering and Methodology10.1145/295509325:3(1-32)Online publication date: 22-Aug-2016
https://dl.acm.org/doi/10.1145/2955093
Mao YBoqin FLi ZYao L(2006)Neural networks based automated test oracle for software testingProceedings of the 13th international conference on Neural information processing - Volume Part III10.5555/1986861.1986921(498-507)Online publication date: 3-Oct-2006
https://dl.acm.org/doi/10.5555/1986861.1986921
Shan LZhu HZhu HHorgan JCheung SLi J(2006)Testing software modelling tools using data mutationProceedings of the 2006 international workshop on Automation of software test10.1145/1138929.1138938(43-49)Online publication date: 23-May-2006
https://dl.acm.org/doi/10.1145/1138929.1138938

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Refinement and Test Case Generation in UTP

Automated test generation from timed automata

Automated test case generation from IFAD VDM++ specifications

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