Temporal Analytics for Software Usage Models

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10729)

Abstract

We address the problem of analysing how users actually interact with software. Users are heterogeneous: they adopt different usage patterns and each individual user may move between different patterns, from one interaction session to another, or even during an interaction session. For analysis, we require new 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.

Notes

Acknowledgement

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

References

  1. 1.
    Kwiatkowska, M.Z., 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).  https://doi.org/10.1007/978-3-642-22110-1_47 CrossRefGoogle Scholar
  2. 2.
    Andrei, O., Calder, M., Chalmers, M., Morrison, A., Rost, M.: Probabilistic formal analysis of app usage to inform redesign. In: Ábrahám, E., Huisman, M. (eds.) IFM 2016. LNCS, vol. 9681, pp. 115–129. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-33693-0_8 CrossRefGoogle Scholar
  3. 3.
    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, Cham (2014).  https://doi.org/10.1007/978-3-319-10696-0_11 Google Scholar
  4. 4.
    Baier, C., Katoen, J.P.: Principles of Model Checking. The MIT Press, Cambridge (2008)MATHGoogle Scholar
  5. 5.
    Murphy, K.P.: Machine Learning: A Probabilistic Perspective. The MIT Press, Cambridge (2012)MATHGoogle Scholar
  6. 6.
    Borges, J., Levene, M.: Data mining of user navigation patterns. In: Masand, B., Spiliopoulou, M. (eds.) WebKDD 1999. LNCS (LNAI), vol. 1836, pp. 92–112. Springer, Heidelberg (2000).  https://doi.org/10.1007/3-540-44934-5_6 CrossRefGoogle Scholar
  7. 7.
    Chierichetti, F., Kumar, R., Raghavan, P., Sarlós, T.: Are web users really Markovian? In: Mille, A., Gandon, F.L., Misselis, J., Rabinovich, M., Staab, S. (eds.) Proceedings of the 21st World Wide Web Conference 2012 (WWW 2012), pp. 609–618. ACM (2012)Google Scholar
  8. 8.
    Singer, P., Helic, D., Taraghi, B., Strohmaier, M.: Detecting memory and structure in human navigation patterns using Markov chain models of varying order. PLoS One 9(7), 1–21 (2014)CrossRefGoogle Scholar
  9. 9.
    Singer, P., Helic, D., Hotho, A., Strohmaier, M.: HypTrails: a Bayesian approach for comparing hypotheses about human trails on the web. In: Gangemi, A., Leonardi, S., Panconesi, A. (eds.) Proceedings of the 24th International Conference on World Wide Web (WWW 2015), pp. 1003–1013. ACM (2015)Google Scholar
  10. 10.
    Ghezzi, C., Pezzè, M., Sama, M., Tamburrelli, G.: Mining behavior models from user-intensive web applications. In: Proceedings of the 36th International Conference on Software Engineering (ICSE 2014), pp. 277–287. ACM (2014)Google Scholar
  11. 11.
    Kostakos, V., Ferreira, D., Gonçalves, J., Hosio, S.: Modelling smartphone usage: a Markov state transition model. In: Lukowicz, P., Krüger, A., Bulling, A., Lim, Y., Patel, S.N. (eds.) Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2016), pp. 486–497 (2016)Google Scholar
  12. 12.
    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)MathSciNetMATHGoogle Scholar
  13. 13.
    Welch, L.: Hidden Markov models and the Baum-Welch algorithm. IEEE Inf. Theory Soc. Newslett. 53(4), 10–13 (2003)Google Scholar
  14. 14.
    Bartocci, E., Grosu, R., Karmarkar, A., Smolka, S.A., Stoller, S.D., Zadok, E., Seyster, J.: Adaptive runtime verification. In: Qadeer, S., Tasiran, S. (eds.) RV 2012. LNCS, vol. 7687, pp. 168–182. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-35632-2_18 CrossRefGoogle Scholar
  15. 15.
    Bell, M., Chalmers, M., Fontaine, L., Higgs, M., Morrison, A., Rooksby, J., Rost, M., Sherwood, S.: Experiences in logging everyday app use. In: ACM Proceedings of Digital Economy 2013 (2013)Google Scholar
  16. 16.
    Zhang, L., Hermanns, H., Jansen, D.N.: Logic and model checking for hidden Markov models. In: Wang, F. (ed.) FORTE 2005. LNCS, vol. 3731, pp. 98–112. Springer, Heidelberg (2005).  https://doi.org/10.1007/11562436_9 CrossRefGoogle Scholar
  17. 17.
    Sucar, L.E.: Probabilistic Graphical Models: Principles and Applications. ACVPR. Springer, London (2015).  https://doi.org/10.1007/978-1-4471-6699-3 CrossRefMATHGoogle Scholar
  18. 18.
    Langmead, C.J.: Generalized queries and Bayesian statistical model checking in dynamic Bayesian networks: application to personalized medicine. In: Proceedings of CSB 2009 (2009)Google Scholar
  19. 19.
    Stoller, S.D., Bartocci, E., Seyster, J., Grosu, R., Havelund, K., Smolka, S.A., Zadok, E.: Runtime verification with state estimation. In: Khurshid, S., Sen, K. (eds.) RV 2011. LNCS, vol. 7186, pp. 193–207. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-29860-8_15 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.School of Computing ScienceUniversity of GlasgowGlasgowUK

Personalised recommendations