Probabilistic Model Checking of DTMC Models of User Activity Patterns
This work addresses software developers' need to understand and anticipate user activity patterns, but it is incremental as it applies existing probabilistic methods to a new domain.
The authors tackled the problem of unpredictable user behavior in software by defining probabilistic models from logged user traces and using probabilistic model checking to analyze software usage, applying it to an iOS app.
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.