Probabilistic Model Checking of DTMC Models of User Activity Patterns
Software Engineering
2014-03-27 v1 Logic in Computer Science
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.
Cite
@article{arxiv.1403.6678,
title = {Probabilistic Model Checking of DTMC Models of User Activity Patterns},
author = {Oana Andrei and Muffy Calder and Matthew Higgs and Mark Girolami},
journal= {arXiv preprint arXiv:1403.6678},
year = {2014}
}