Probabilistic Formal Analysis of App Usage to Inform Redesign
This work addresses app redesign for developers and designers by providing a method to analyze usage data, though it is incremental in bridging stochastic and formal modeling.
The paper tackles the problem of understanding app usage patterns to inform redesign by inferring Markov models from logs and applying probabilistic formal analysis, revealing that users interleave activity patterns over time, which supports integrated redesign rather than separate app versions.
This paper sets out a process of app analysis intended to support understanding of use but also redesign. From usage logs we infer activity patterns - Markov models - and employ probabilistic formal analysis to ask questions about the use of the app. The core of this paper's contribution is a bridging of stochastic and formal modelling, but we also describe the work to make that analytic core utile within a design team. We illustrate our work via a case study of a mobile app presenting analytic findings and discussing how they are feeding into redesign. We had posited that two activity patterns indicated two separable sets of users, each of which might benefit from a differently tailored app version, but our subsequent analysis detailed users' interleaving of activity patterns over time - evidence speaking more in favour of redesign that supports each pattern in an integrated way. We uncover patterns consisting of brief glances at particular data and recommend them as possible candidates for new design work on widget extensions: small displays available while users use other apps.