Temporal analytics for software usage models

Andrei, O. and Calder, M. (2018) Temporal analytics for software usage models. Lecture Notes in Computer Science, 10729, pp. 9-24. (doi: 10.1007/978-3-319-74781-1_1)

144355.pdf - Accepted Version



We address the problem of analysing how users actually interact with software. Users are heterogeneous: they adopt different usage styles and each individual user may move between different styles, from one interaction session to another, or even during an interaction session. For analysis, we require new temporal analytics: techniques to model and analyse temporal data sets of logged interactions with the purpose of discovering, interpreting, and communicating meaningful patterns of usage. We define new probabilistic models whose parameters are inferred from logged time series data of user-software interactions. We formulate hypotheses about software usage together with the developers, encode them in probabilistic temporal logic, and analyse the models according to the probabilistic properties. We illustrate by application to logged data from a deployed mobile application software used by thousands of users.

Item Type:Articles
Additional Information:6th International Symposium “From Data to Models and Back (DataMod)” (DataMod 2017, Satellite event of SEFM 2017), Trento, Italy, 4-5 Sept 2017
Glasgow Author(s) Enlighten ID:Calder, Professor Muffy and Andrei, Dr Oana
Authors: Andrei, O., and Calder, M.
College/School:College of Science and Engineering > School of Computing Science
Journal Name:Lecture Notes in Computer Science
Published Online:02 February 2018
Copyright Holders:Copyright © 2018 Springer International Publishing
First Published:First published in Lecture Notes in Computer Science 10729:9-24
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher
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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
566711A population approach to ubicomp system designMatthew ChalmersEngineering and Physical Sciences Research Council (EPSRC)EP/J007617/1COM - COMPUTING SCIENCE