Probabilistic Formal Analysis of App Usage to Inform Redesign

  • Oana AndreiEmail author
  • Muffy Calder
  • Matthew Chalmers
  • Alistair Morrison
  • Mattias Rost
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9681)


Evaluation and redesign of user-intensive mobile applications is challenging because users are often heterogeneous, adopting different patterns of activity, at different times. We set out a process of integrating statistical, longitudinal analysis of actual logged behaviours, formal, probabilistic discrete state models of activity patterns, and hypotheses over those models expressed as probabilistic temporal logic properties to inform redesign. We employ formal methods not to the design of the mobile application, but to characterise the different probabilistic patterns of actual use over various time cuts within a population of users. We define the whole process from identifying questions that give us insight into application usage, to event logging, data abstraction from logs, model inference, temporal logic property formulation, visualisation of results, and interpretation in the context of redesign. We illustrate the process through a real-life case study, which results in a new and principled way for selecting content for an extension to the mobile application.


Activity Pattern Atomic Proposition Menu Item Main Menu Viewing Pattern 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This research is supported by the EPSRC Programme Grant A Population Approach to Ubicomp System Design (EP/J007617/1).


  1. 1.
    Baier, C., Katoen, J.P.: Principles of Model Checking. The MIT Press, Cambridge (2008)zbMATHGoogle Scholar
  2. 2.
    Bell, M., Chalmers, M., Fontaine, L., Higgs, M., Morrison, A., Rooksby, J., Rost, M., Sherwood, S.: Experiences in logging everyday App. use. In: Proceedings of Digital Economy 2013. ACM (2013)Google Scholar
  3. 3.
    Girolami, M., Kabán, A.: Simplicial mixtures of Markov chains: distributed modelling of dynamic user profiles. In: Thrun, S., Saul, L.K., Schölkopf, B. (eds.) Advances in Neural Information Processing Systems 16 (NIPS 2003), pp. 9–16. MIT Press (2004)Google Scholar
  4. 4.
    Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Stat. Soc. Ser. B (Methodol.) 39(1), 1–38 (1977)MathSciNetzbMATHGoogle Scholar
  5. 5.
    Kwiatkowska, M., Norman, G., Parker, D.: Stochastic model checking. In: Bernardo, M., Hillston, J. (eds.) SFM 2007. LNCS, vol. 4486, pp. 220–270. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  6. 6.
    Andrei, O., Calder, M., Higgs, M., Girolami, M.: Probabilistic model checking of DTMC models of user activity patterns. In: Norman, G., Sanders, W. (eds.) QEST 2014. LNCS, vol. 8657, pp. 138–153. Springer, Heidelberg (2014)Google Scholar
  7. 7.
    Kwiatkowska, M., Norman, G., Parker, D.: PRISM 4.0: verification of probabilistic real-time systems. In: Gopalakrishnan, G., Qadeer, S. (eds.) CAV 2011. LNCS, vol. 6806, pp. 585–591. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  8. 8.
    Hall, M., Bell, M., Morrison, A., Reeves, S., Sherwood, S., Chalmers, M.: Adapting ubicomp software and its evaluation. In: Graham, T.C.N., Calvary, G., Gray, P.D. (eds.) Proceedings of EICS 2009, pp. 143–148. ACM (2009)Google Scholar
  9. 9.
    von Mayrhauser, A., Vans, A.M.: Program comprehension during software maintenance and evolution. IEEE Comput. 28(8), 44–55 (1995)CrossRefGoogle Scholar
  10. 10.
    Fittkau, F., Waller, J., Wulf, C., Hasselbring, W.: Live trace visualization for comprehending large software landscapes: the ExplorViz approach. In: Telea, A., Kerren, A., Marcus, A. (eds.) Proceedings of VISSOFT 2013, pp. 1–4 (2013)Google Scholar
  11. 11.
    Gomez, L., Neamtiu, I., Azim, T., Millstein, T.D.: RERAN: timing- and touch-sensitive record and replay for Android. In: Notkin, D., Cheng, B.H.C., Pohl, K. (eds.) Proceedings of ICSE 2013, pp. 72–81. IEEE/ACM (2013)Google Scholar
  12. 12.
    Beschastnikh, I., Brun, Y., Ernst, M.D., Krishnamurthy, A.: Inferring models of concurrent systems from logs of their behavior with CSight. In: Jalote, P., Briand, L.C., van der Hoek, A. (eds.) Proceedings of ICSE 2014, Hyderabad, India, pp. 468–479. ACM (2014)Google Scholar
  13. 13.
    Ghezzi, C., Pezzè, M., Sama, M., Tamburrelli, G.: Mining behavior models from user-intensive web applications. In: Proceedings of ICSE 2014, Hyderabad, India, pp. 277–287. ACM (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Oana Andrei
    • 1
    Email author
  • Muffy Calder
    • 1
  • Matthew Chalmers
    • 1
  • Alistair Morrison
    • 1
  • Mattias Rost
    • 1
  1. 1.School of Computing ScienceUniversity of GlasgowGlasgowUK

Personalised recommendations