Probabilistic Formal Analysis of App Usage to Inform Redesign

TL;DR

This paper introduces a method combining stochastic and formal modeling of app usage logs to analyze user behavior and inform app redesign, demonstrated through a case study on a mobile app.

Contribution

It bridges probabilistic and formal analysis methods for app usage, enabling detailed insights to guide redesign decisions within development teams.

Findings

Identified interleaving of user activity patterns over time.

Evidence supports redesigning to support integrated activity patterns.

Recommended widget extensions based on usage patterns.

Abstract

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.