NDLI: Calculating the usage probabilities of statistical usage models by constraints optimization

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Calculating the usage probabilities of statistical usage models by constraints optimization

Calculating the usage probabilities of statistical usage models by constraints optimization

Content Provider	ACM Digital Library
Author	Holpp, Stefan Götz, Helmut Dulz, Winfried German, Reinhard
Abstract	The systematic generation of test cases from statistical usage models has been investigated recently for specific application domains, such as wireless communications or automotive applications. For Markov chain usage models, the expected usage of a hardware/software system is represented by transitions between usage states and a usage profile, meaning probability values that are attached to the state transitions. In this paper, we explain how to calculate the profile probabilities for the Markov chain usage model from a set of linear usage constraints and by optimizing a convex polyhedron that represents the constrained solution space. Comparing the computed probability distributions of our polyhedron approach with the maximum entropy technique, which is the main technique used so far, illustrates that our results are more obvious to the intented constraint semantics. In order to demonstrate the applicability of our approach, workflow testing of a complex RIS/PACS system in the medical domain was carried through and has provided promising results.
Starting Page	127
Ending Page	134
Page Count	8
File Format	PDF
ISBN	9781605589701
DOI	10.1145/1808266.1808285
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2010-05-03
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Medical case study Markov chain usage model Optimizing usage probabilities Profile generation Polyhedron optimization Statistical usage testing Usage constraints
Content Type	Text
Resource Type	Article

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