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Probabilistic Model Checking of DTMC Models of User Activity Patterns

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Type
Preprint
Publication Date
Submission Date
Identifiers
arXiv ID: 1403.6678
Source
arXiv
License
Yellow
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Abstract

Software developers cannot always anticipate how users will actually use their software as it may vary from user to user, and even from use to use for an individual user. In order to address questions raised by system developers and evaluators about software usage, we define new probabilistic models that characterise user behaviour, based on activity patterns inferred from actual logged user traces. We encode these new models in a probabilistic model checker and use probabilistic temporal logics to gain insight into software usage. We motivate and illustrate our approach by application to the logged user traces of an iOS app.

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