Advertisement

Two Approaches for the Computational Model for Software Usability in Practice

  • Eva RakovskáEmail author
  • Miroslav Hudec
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 945)

Abstract

Rapid software development and its massive deployment into practice brings a lot of problems and challenges. How to evaluate and manage the existing software in an enterprise is not an easy task. Despite different methodologies in IT management, we encounter problems with how to measure usability of software. Software usability is based on user experience and it is strongly subjective. Every IT user is unique, so the measurement of IT usability has often qualitative character. The main tool for such measurement is survey, which maps her or his needs of daily work. The article comes from experimental study in the medium-sized company. It was based on the idea of using rule-based expert system for measurement of software usability in enterprises. Experimental study gave a more detailed view into the problem; how to design the fuzzy-rules and how to compute them. The article points to problems in designing a computational model of software usability measurement. Thus, it suggests a computational model, which is able to avoid the main problems arising from experimental study and to deal with the uncertainty and vagueness of IT user experience, different number of questions for each users group, different ranges of categorical answers among groups, and variations in the number of answered questionnaires. This model is based on the three hierarchical levels of aggregation with the support of fuzzy logic.

Keywords

IT software usability Usability aggregation Fuzzy quantifiers Uninorm 

Notes

Acknowledgements

This paper is part of a project VEGA No. 1/0373/18 entitled “Big data analytics as a tool for increasing the competitiveness of enterprises and supporting informed decisions” by the Ministry of Education, Science, Research and Sport of the Slovak Republic.

References

  1. 1.
    Albert, W., Tullis, T.: Measuring the User Experience, Collecting, Analyzing, and Presenting Usability Metrics (Interactive Technologies), 2nd edn. Elsevier, Waltham (2013)Google Scholar
  2. 2.
    Bandarian, R.: Evaluation of Commercial potential of a new technology at the early stage of development with fuzzy logic. J. Technol. Manag. Innov. 2(4), 73–85 (2007)Google Scholar
  3. 3.
    Bavdaž, M.: Sources of measurement errors in business surveys. J. Official Stat. 26(1), 25–42 (2010)Google Scholar
  4. 4.
    Bavdaž, M, Biffignandi, S., Bolko, I., Giesen, D., Gravem, D., Haraldsen, G., et al.: Final report integrating findings on business perspectives related to NSIs’ statistics. Deliverable 3.2., Blue-Ets Project (2011). http://www.blue-ets.istat.it/fileadmin/deliverables/Deliverable3.2.pdf. Accessed June 2016
  5. 5.
    Beliakov, G., Pradera, A., Calvo, T.: Aggregation Functions: A Guide for Practitioners. Springer, Heidelberg (2007)zbMATHGoogle Scholar
  6. 6.
    Calvo, T., Kolesárová, A., Komorníková, M., Mesiar, R.: Aggregation operators: properties, classes and construction methods. In: Calvo, T., Mayor, G., Mesiar, R. (eds.) Aggregation Operators. New Trends and Applications, pp. 3–104. Physica-Verlag, Heidelberg (2002)CrossRefGoogle Scholar
  7. 7.
    Detyniecki, M., Fundamentals on aggregation operators. In: Proceedings of the AGOP 2001, Asturias (2001)Google Scholar
  8. 8.
    Dubois, D., Prade, H.: A review of fuzzy set aggregation connectives. Inf. Sci. 36, 85–121 (1985)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Greiner, L., White, S.K.: What is ITIL? Your guide to the IT Infrastructure Library, in digital magazine CIO from IDG. https://www.cio.com/article/2439501/itil/infrastructure-it-infrastructure-library-itil-definition-and-solutions.html. Accessed 28 Mar 2018
  10. 10.
    Herrera, F., Martíez, L.: A model based on linguistic 2-tuples for dealing with multigranular hierarchical linguistic contexts in multiexpert decision-making. IEEE Trans. Syst. Man Cybern. Part B Cybern. 31, 227–234 (2001)CrossRefGoogle Scholar
  11. 11.
    ISACA, Service IT governance professionals, COBIT5, an ISACA framework. http://www.isaca.org/cobit/pages/default.aspx. Accessed 28 Mar 2018
  12. 12.
    Králiková, L.: Testovanie efektívnosti softvéru v podnikovej praxi z hľadiska užívateľov = Software effectiveness testing in business practice from a users` perspective, (in Slovak), Master Thesis, University of Economic in Bratislava (2017)Google Scholar
  13. 13.
    Pavlík, L.: Metrics for Evaluating Information Systems, Posterus, portál pre odborné publikovanie, ISSN; 1338-0087 http://www.posterus.sk/?p=18957. Accessed 21 Mar 2018
  14. 14.
    Rakovská, E.: Projekty znalostného manažmentu ako súčasť metodiky Balanced scorecard = Knowledge management projects as a part of Balanced scorecard methodology In: Eduard Hyránek, E., Nagy, L., Výsledky riešenia končiacich grantových úloh VEGA 1/0261/10, 1/0872/09, 1/0384/10, 1/0415/10: zborník vedeckých statí. EKONÓM, Bratislava (2011)Google Scholar
  15. 15.
    Rakovská, E., Hudec, M.: Two approaches for the computational model for software usability in practice. In: Kulczycki, P., Kowalski, P.A., Łukasik, S. (eds.) Contemporary Computational Science, p. 21. AGH-UST Press, Cracow (2018)Google Scholar
  16. 16.
    Ruspini, E.: A new approach to clustering. Inf. Control 15, 22–32 (1969)CrossRefGoogle Scholar
  17. 17.
    Tudorie, C., Qualifying objects in classical relational database querying. In: Galindo, J. (ed.) Handbook of Research on Fuzzy Information Processing in Databases. Information Science Reference, pp. 218–245. Hershey (2008)Google Scholar
  18. 18.
    Yager, R.R.: A new approach to the summarization of data. Inf. Sci. 28, 69–86 (1982)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Yager, R.R., Rybalov, A.: Uninorm aggregation operators. Fuzzy Sets Syst. 80, 111–120 (1996)MathSciNetCrossRefGoogle Scholar
  20. 20.
    QP-Quality Progress, the official publication of ASQ. Allen, I.E., Seaman, Ch.A.: Likert Scales and Data Analyses (2007). http://asq.org/quality-progress/2007/07/statistics/likert-scales-and-data-analyses.html. Accessed 29 Mar 2018

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Faculty of Economic InformaticsUniversity of Economics in BratislavaBratislavaSlovakia

Personalised recommendations