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Assessing Subjective Quality of Web Interaction with Neural Network as Context of Use Model

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Digital Transformation and Global Society (DTGS 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 745))

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Abstract

Despite certain advances in automation of software quality assurance, testing and debugging remain the most laborious activities in the software development cycle. Evaluation of web interaction quality is still largely performed with traditional human effort-intensive methods, particularly due to the inevitable association of website usability with particular contexts of use, target users, tasks, etc. We believe that testing automation in this field may ultimately lead to better online experience for all and are important in promoting e-society development. We propose to employ artificial neural networks to predict website users’ subjective impressions, whose importance is widely recognized but that are somehow overshadowed by the effectiveness and efficiency dimensions. We justify the structure of the network, with the input layer reflecting context of use, while the output layer consisting of the subjective evaluation scales (Beautiful, Evident, Fun, Trustworthy, and Usable). The experimental session with 82 users and 21 university websites was undertaken to collect the evaluation data for the network training. Finally, we verify the validity of the model by comparing it to a certain baseline, analyze the importance of the input factors, and provide recommendations for future evaluations-collecting sessions.

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References

  1. Glass, R.L.: Facts and Fallacies of Software Engineering. Addison-Wesley Professional, Boston (2002)

    Google Scholar 

  2. Gay, G., Li, C.Q.: AChecker: open, interactive, customizable, web accessibility checking. In: 2010 International Cross Disciplinary Conference on Web Accessibility (W4A 2010), no. 23 (2010)

    Google Scholar 

  3. Bakaev, M., Mamysheva, T., Gaedke, M.: Current Trends in automating usability evaluation of websites. In: International Forum on Strategic Technologies (IFOST), NSTU, pp. 510–514 (2016)

    Google Scholar 

  4. Bakaev, M., Gaedke, M.: Application of evolutionary algorithms in interaction design: from requirements and ontology to optimized web interface. In: IEEE ElConRusNW, pp. 125–130. LETI, St. Petersburg (2016). doi:10.1109/EIConRusNW.2016.7448138

  5. Dingli, A., Mifsud, J.: Useful: a framework to mainstream web site usability through automated evaluation. Int. J. Hum. Comput. Interact. (IJHCI), 2(1), 10–30 (2011)

    Google Scholar 

  6. The Encyclopedia of Human-Computer Interaction, 2nd edn. (2016). https://www.interaction-design.org/literature/book/the-encyclopedia-of-human-computer-interaction-2nd-ed. Accessed 07 Apr 2017

  7. Vanderdonckt, J., Beirekdar, A., Noirhomme-Fraiture, M.: Automated evaluation of web usability and accessibility by guideline review. In: Koch, N., Fraternali, P., Wirsing, M. (eds.) ICWE 2004. LNCS, vol. 3140, pp. 17–30. Springer, Heidelberg (2004). doi:10.1007/978-3-540-27834-4_4

    Chapter  Google Scholar 

  8. Grigera, J., Garrido, A., Rivero, J.M., Rossi, G.: Automatic detection of usability smells in web applications. Int. J. Hum.-Comput. Stud. 97, 129–148 (2017)

    Article  Google Scholar 

  9. Lin, Y.C., Yeh, C.H., Wei, C.C.: How will the use of graphics affect visual aesthetics? A user-centered approach for web page design. Int. J. Hum.-Comput. Stud. 71(3), 217–227 (2013)

    Article  Google Scholar 

  10. Feuerstack, S., Blumendorf, M., Kern, M., Kruppa, M., Quade, M., Runge, M., Albayrak, S.: Automated usability evaluation during model-based interactive system development. In: Forbrig, P., Paternò, F. (eds.) HCSE/TAMODIA-2008. LNCS, vol. 5247, pp. 134–141. Springer, Heidelberg (2008). doi:10.1007/978-3-540-85992-5_12

    Google Scholar 

  11. Palanque, P., Barboni, E., Martinie, C., Navarre, D., Winckler, M.: A model-based approach for supporting engineering usability evaluation of interaction techniques. In: 3rd ACM SIGCHI symposium on Engineering Interactive Computer Systems, pp. 21–30 (2011)

    Google Scholar 

  12. Pleuss, A., Wollny, S., Botterweck, G.: Model-driven development and evolution of customized user interfaces. In: 5th ACM SIGCHI Symposium on Engineering Interactive Computing Systems, pp. 13–22 (2013). doi:10.1145/2494603.2480298

  13. Speicher, M., Both, A., Gaedke, M.: Ensuring web interface quality through usability-based split testing. In: Casteleyn, S., Rossi, G., Winckler, M. (eds.) ICWE 2014. LNCS, vol. 8541, pp. 93–110. Springer, Cham (2014). doi:10.1007/978-3-319-08245-5_6

    Google Scholar 

  14. Viswanathan, S., Peters, J.C.: Automating UI guidelines verification by leveraging pattern based UI and model based development. In: ACM CHI 2010 EA on Human Factors in Computing Systems, pp. 4733–4742 (2010). doi:10.1145/1753846.1754222

  15. Barrera-León, L., et al.: TUKUCHIY: a dynamic user interface generator to improve usability. Int. J. Web Inf. Syst. 12(2), 150–176 (2016)

    Article  Google Scholar 

  16. Vanmali, M., Last, M., Kandel, A.: Using a neural network in the software testing process. Int. J. Intell. Syst. 17(1), 45–62 (2002). doi:10.1002/int.1002

    Article  MATH  Google Scholar 

  17. Dias, J., et al.: A genetic algorithm with neural network fitness function evaluation for IMRT beam angle optimization. Central Eur. J. Oper. Res. 22(3), 431–455 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  18. Yao, X.: Evolving artificial neural networks. Proc. IEEE 87(9), 1423–1447 (1999)

    Article  Google Scholar 

  19. Guo, F., et al.: Optimization design of a webpage based on Kansei engineering. Hum. Factors Ergon. Manuf. Serv. Ind. 26(1), 110–126 (2016). doi:10.1002/hfm.20617

    Article  Google Scholar 

  20. Bakaev, M., Gaedke, M., Heil, S.: Kansei engineering experimental research with university websites. Technical report, TU Chemnitz, CSR-16-01 (2016)

    Google Scholar 

  21. Bakaev, M., Avdeenko, T.: User interface design guidelines arrangement in a recommender system with frame ontology. In: Yu, H., Yu, G., Hsu, W., Moon, Y.-S., Unland, R., Yoo, J. (eds.) DASFAA 2012. LNCS, vol. 7240, pp. 311–322. Springer, Heidelberg (2012). doi:10.1007/978-3-642-29023-7_31

    Chapter  Google Scholar 

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Acknowledgement

The reported study was funded by RFBR according to the research project No. 16-37-60060 mol_a_dk. The authors also thank M. Gaedke and S. Heil from TU Chemnitz who facilitated the collection of user evaluations.

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Authors and Affiliations

  1. Novosibirsk State Technical University, Novosibirsk, Russia

    Maxim Bakaev, Vladimir Khvorostov & Tatiana Laricheva

Authors
  1. Maxim Bakaev
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  2. Vladimir Khvorostov
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  3. Tatiana Laricheva
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Corresponding author

Correspondence to Maxim Bakaev .

Editor information

Editors and Affiliations

  1. National Research University Higher School of Economics, St. Petersburg, Russia

    Daniel A. Alexandrov

  2. ITMO University, St. Petersburg, Russia

    Alexander V. Boukhanovsky

  3. ITMO University, St. Petersburg, Russia

    Andrei V. Chugunov

  4. National Research University Higher School of Economics, St. Petersburg, Russia

    Yury Kabanov

  5. National Research University Higher School of Economics, St. Petersburg, Russia

    Olessia Koltsova

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Bakaev, M., Khvorostov, V., Laricheva, T. (2017). Assessing Subjective Quality of Web Interaction with Neural Network as Context of Use Model. In: Alexandrov, D., Boukhanovsky, A., Chugunov, A., Kabanov, Y., Koltsova, O. (eds) Digital Transformation and Global Society. DTGS 2017. Communications in Computer and Information Science, vol 745. Springer, Cham. https://doi.org/10.1007/978-3-319-69784-0_16

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  • DOI: https://doi.org/10.1007/978-3-319-69784-0_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69783-3

  • Online ISBN: 978-3-319-69784-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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