Skip to main content
SpringerLink
Log in

Understanding Process Models Using the Eye-Tracking: A Systematic Mapping

  • Conference paper
  • First Online:
Book cover Quality of Information and Communications Technology (QUATIC 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1010))

  • 830 Accesses

Abstract

Business process modeling can involve multiple stakeholders, so it is natural that problems may occur in building and understanding them. One way to perceive these problems is to evaluate the comprehension of these models through the collection of data related to the readers’ awareness with an eye-tracking device. This device allows collecting data of specific facial reactions of the people, such as the movement of the eyes and dilation of the pupils and the number of blinks in a specified time interval. The objective of this paper is to provide an overview of researches that evaluate the understanding of process models through eye-tracking techniques. A systematic mapping study was developed to achieve this goal, following the best practices in the area of Software Engineering. This study consolidated 19 papers for the analysis and extraction of data from the 1,161 studies initially found.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 64.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 84.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Vaknin, M., Filipowska, A.: Information quality framework for the design and validation of data flow within business processes - position paper. In: International Conference on Business Information Systems, vol. 8787, pp. 158–168. Springer, Heidelberg (2017)

    Google Scholar 

  2. Alotaibi, Y., Liu, F.: Survey of business process management: challenges and solutions. Enterprise Inf. Syst. 11(8), 1119–1153 (2016)

    Article  Google Scholar 

  3. Melcher, J., Seese, D.: Towards validating prediction systems for process understandability: measuring process understandability. In: Proceedings of the 2008 10th SYNASC 2008, Anais, Timisoara. IEEE (2008)

    Google Scholar 

  4. Jiménez-ramírez, A., Weber, B., Barba, I., Del Valle, C.: Generating optimized configurable business process models in scenarios subject to uncertainty. Inf. Softw. Technol. 57(1), 571–594 (2015)

    Article  Google Scholar 

  5. Unterkalmsteiner, M., Gorschek, T., Islam, A.K.M.M., Cheng, C.K., Permadi, R.B., Feldt, R.: Evaluation and measurement of software process improvement: a systematic literature review. IEEE Trans. Softw. Eng. 38, 398–424 (2011)

    Article  Google Scholar 

  6. Gibson, D.L., Goldenson, D.R., Kost, K.: Performance results of CMMI-based process improvement. Carnegie-Mellon University, Pittsburgh, PA, Software Engineering Institute (2006)

    Google Scholar 

  7. Mohd, N., Ahmad, R., Hassan, N.: Resistance factors in the implementation of software process improvement project. J. Comput. Sci. 4, 211–219 (2008)

    Google Scholar 

  8. Hani, S.U.: Impact of process improvement on software development predictions, for measuring software development project’s performance benefits. In: Proceedings of the 7th International Conference on Frontiers of Information Technology, p. 54 (2009)

    Google Scholar 

  9. Mendoza, V., Silveira, D.S., Albuquerque, M.L., Araújo, J.: Verifying BPMN understandability with novice business. In: 33rd Symposium on Applied Computing - ACM/SIGAPP, Pau – France, pp. 94–101. ACM (2018)

    Google Scholar 

  10. Rodrigues, R.D.A., Barros, M.D.O., Revoredo, K., Azevedo, L.G., Leopold, H.: An experiment on process model understandability using textual work instructions and BPMN models. In: 29th SBES, pp. 41–50 (2015)

    Google Scholar 

  11. Figl, K., Recker, J.: Exploring cognitive style and task-specific preferences for process representations. Requirements Eng. 21(1), 63–85 (2014)

    Article  Google Scholar 

  12. Mendling, J., Reijers, H.A., Cardoso, J.: What makes process models understandable? In: Alonso, G., Dadam, P., Rosemann, M. (eds.) BPM 2007. LNCS, vol. 4714, pp. 48–63. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-75183-0_4

    Chapter  Google Scholar 

  13. Kitchenham, B.A., Dyba, T., Jorgensen, M.: Evidence-based software engineering. In: Proceedings of the 26th International Conference on Software Engineering, pp. 273–281. IEEE Computer Society (2004)

    Google Scholar 

  14. Petersen, K., Feldt, R., Mujtaba, S., Mattsson, M.: Systematic mapping studies in software engineering. In: Proceedings of the 12th International Conference on Evaluation and Assessment in Software Engineering, EASE 2008, vol. 8, pp. 68–77 (2008)

    Google Scholar 

  15. Ko, R.K.L.: A computer scientist’s introductory guide to business process management (BPM), XRDS: Crossroads. ACM Mag. Students 15(4), 4 (2009)

    Google Scholar 

  16. Wahl, T., Sindre, G.: An analytical evaluation of BPMN using a semiotic quality framework. In: Advanced Topics in Database Research, vol. 5, pp. 94–105 (2006)

    Google Scholar 

  17. Laue, R., Gadatsch, A.: Measuring the understandability of business process models - are we asking the right questions? In: zur Muehlen, M., Su, J. (eds.) BPM 2010. LNBIP, vol. 66, pp. 37–48. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-20511-8_4

    Chapter  Google Scholar 

  18. Sharafi, Z., Shaffer, T., Sharif, B.: Eye-tracking metrics in software engineering. In: Asia-Pacific Software Engineering Conference – APSEC, pp. 96–103 (2015)

    Google Scholar 

  19. Moody, D.: The “physics” of notations: toward a scientific basis for constructing visual notations in software engineering. IEEE Trans. Softw. Eng. 35(6), 756–779 (2009)

    Article  Google Scholar 

  20. Kitchenham, B., Charters, S.: Guidelines for performing systematic literature reviews in software engineering. Technical report EBSE 2007-001, Keele University and Durham University Joint Report (2007)

    Google Scholar 

  21. OMG BPMN2, Business Process Model and Notation (BPMN) v2.0, Object Management Group (2011)

    Google Scholar 

  22. Da Silva, F.Q., et al.: Replication of empirical studies in software engineering research: a systematic mapping study. Empirical Softw. Eng. 19(3), 501–557 (2014)

    Google Scholar 

  23. Wang, W.: The effect of rule linking on business process model understanding. In: Wang, W. (ed.) Integrating Business Process Models and Rules. LNBIP, vol. 343, pp. 42–59. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11809-9_5

    Chapter  Google Scholar 

  24. Abbad Andaloussi, A., Slaats, T., Burattin, A., Hildebrandt, T.T., Weber, B.: Evaluating the understandability of hybrid process model representations using eye tracking: first insights. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) BPM 2018. LNBIP, vol. 342, pp. 475–481. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11641-5_37

    Chapter  Google Scholar 

  25. Boutin, K.-D., Léger, P.-M., Davis, C.J., Hevner, A.R., Labonté-LeMoyne, É.: Attentional characteristics of anomaly detection in conceptual modeling. In: Davis, F.D., Riedl, R., vom Brocke, J., Léger, P.-M., Randolph, A.B. (eds.) Information Systems and Neuroscience. LNISO, vol. 29, pp. 57–63. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-01087-4_7

    Chapter  Google Scholar 

  26. Zimoch, M., et al.: Utilizing the capabilities offered by eye-tracking to foster novices’ comprehension of business process models. In: Xiao, J., Mao, Z.-H., Suzumura, T., Zhang, L.-J. (eds.) ICCC 2018. LNCS, vol. 10971, pp. 155–163. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-94307-7_12

    Chapter  Google Scholar 

  27. Vermeulen, S.: Real-time business process model tailoring: the effect of domain knowledge on reading strategy. In: Debruyne, C., et al. (eds.) OTM 2017. LNCS, vol. 10697, pp. 280–286. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-73805-5_30

    Chapter  Google Scholar 

  28. Zimoch, M., Mohring, T., Pryss, R., Probst, T., Schlee, W., Reichert, M.: Using insights from cognitive neuroscience to investigate the effects of event-driven process chains on process model comprehension. In: Teniente, E., Weidlich, M. (eds.) BPM 2017. LNBIP, vol. 308, pp. 446–459. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-74030-0_35

    Chapter  Google Scholar 

  29. Pavlicek, J., Hronza, R., Pavlickova, P., Jelinkova, K.: The business process model quality metrics. In: Pergl, R., Lock, R., Babkin, E., Molhanec, M. (eds.) EOMAS 2017. LNBIP, vol. 298, pp. 134–148. Springer, Heidelberg (2017). https://doi.org/10.1007/978-3-319-68185-6_10

    Google Scholar 

  30. Weber, B., Neurauter, M., Burattin, A., Pinggera, J., Davis, C.: Measuring and explaining cognitive load during design activities: a fine-grained approach. In: Davis, F.D., Riedl, R., vom Brocke, J., Léger, P.-M., Randolph, A.B. (eds.) Information Systems and Neuroscience. LNISO, vol. 25, pp. 47–53. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-67431-5_6

    Chapter  Google Scholar 

  31. Petrusel, R., Mendling, J., Reijers, H.A.: How visual cognition influences process model comprehension. Decis. Support Syst. 96, 1–16 (2017)

    Article  Google Scholar 

  32. Burattin, A., Kaiser, M., Neurauter, M., Weber, B.: Eye tracking meets the process of process modeling: a visual analytic approach. In: Dumas, M., Fantinato, M. (eds.) BPM 2016. LNBIP, vol. 281, pp. 461–473. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-58457-7_34

    Chapter  Google Scholar 

  33. Zimoch, M., Pryss, R., Schobel, J., Reichert, M.: Eye tracking experiments on process model comprehension: lessons learned. In: Reinhartz-Berger, I., Gulden, J., Nurcan, S., Guédria, W., Bera, P. (eds.) Enterprise, Business-Process and Information Systems Modeling, BPMDS 2017, EMMSAD. LNBIP, vol. 287, pp. 153–168. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59466-8_10

    Chapter  Google Scholar 

  34. Zimoch, M., Pryss, R., Probst, T., Schlee, W., Reichert, M.: Cognitive insights into business process model comprehension: preliminary results for experienced and inexperienced individuals. In: Reinhartz-Berger, I., Gulden, J., Nurcan, S., Guédria, W., Bera, P. (eds.) BPMDS/EMMSAD -2017. LNBIP, vol. 287, pp. 137–152. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59466-8_9

    Chapter  Google Scholar 

  35. Petrusel, R., Mendling, J., Reijers, H.A.: Task-specific visual cues for improving process model understanding. Inf. Softw. Technol. 79, 63–78 (2016)

    Article  Google Scholar 

  36. Weber, B., et al.: Measuring cognitive load during process model creation. In: Davis, F.D., Riedl, R., vom Brocke, J., Léger, P.-M., Randolph, A.B. (eds.) Information Systems and Neuroscience. LNISO, vol. 10, pp. 129–136. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18702-0_17

    Chapter  Google Scholar 

  37. Petrusel, R., Mendling, J.: Eye-tracking the factors of process model comprehension tasks. In: Salinesi, C., Norrie, M.C., Pastor, Ó. (eds.) CAiSE 2013. LNCS, vol. 7908, pp. 224–239. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38709-8_15

    Chapter  Google Scholar 

  38. Pinggera, J., et al.: Investigating the process of process modeling with eye movement analysis. In: La Rosa, M., Soffer, P. (eds.) BPM 2012. LNBIP, vol. 132, pp. 438–450. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36285-9_46

    Chapter  Google Scholar 

  39. Molina, A.I., Redondo, M.A., Ortega, M., Lacave, C.: Evaluating a graphical notation for modeling collaborative learning activities: a family of experiments. Sci. Comput. Program. 88, 54–81 (2014)

    Article  Google Scholar 

  40. Burattin, A., Kaiser, M., Neurauter, M., Weber, B.: Learning process modeling phases from modeling interactions and eye tracking data. Data Knowl. Eng. 121, 1–17 (2019)

    Article  Google Scholar 

  41. Chen, T., Wang, W., Indulska, M., Sadiq, S.: Business process and rule integration approaches - an empirical analysis. In: Weske, M., Montali, M., Weber, I., vom Brocke, J. (eds.) BPM 2018. LNBIP, vol. 329, pp. 37–52. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98651-7_3

    Chapter  Google Scholar 

  42. Santos, M.C.D.F.: Avaliação da Eficácia Cognitiva de Modelos de Requisitos Orientados a Objetivos. Masters Dissertation, Faculdade de Ciência e Tecnologia Universidade nova de Lisboa (2016)

    Google Scholar 

  43. Sharafi, Z., Soh, Z., Guéhéneuc, Y.G.: A systematic literature review on the usage of eye-tracking in software engineering. Inf. Softw. Technol. 67, 79–107 (2015)

    Article  Google Scholar 

  44. Scheer, A.-W., Nüttgens, M.: ARIS architecture and reference models for business process management. In: van der Aalst, W., Desel, J., Oberweis, A. (eds.) Business Process Management. LNCS, vol. 1806, pp. 376–389. Springer, Heidelberg (2000). https://doi.org/10.1007/3-540-45594-9_24

    Chapter  Google Scholar 

  45. Petri, C.A.: Kommunikation mit Automaten. Ph.D. thesis, Institut fur Instrumentelle Mathematik (1962)

    Google Scholar 

  46. Sommer, M.: Zeitliche Darstellung und Modellierung von Prozessenmithilfe von Gantt-Diagrammen. Bachelors thesis, Ulm University (2012)

    Google Scholar 

  47. Lacaze, X., Philippe, P.: Comprehensive handling of temporal issues in tasks models: what is needed and how to support it. In: Workshop ‘The Temporal Aspects of Work for HCI (CHI 2004)’. Vienna, Austria (2004)

    Google Scholar 

  48. Paternò, F.: ConcurTaskTrees: an engineered notation for task models. In: The Handbook of Task Analysis for Human-Computer Interaction, pp. 483–503 (2004)

    Google Scholar 

  49. Dikici, A., Turetken, O., Demirors, O.: Factors influencing the understandability of process models: a systematic literature review. Inf. Softw. Technol. 93, 112–129 (2018)

    Article  Google Scholar 

  50. Figl, K.: Comprehension of procedural visual business process models. Bus. Inf. Syst. Eng. 59(1), 41–67 (2017)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, University of Pernambuco, Recife, Brazil

    Vinícius Brito, Rafael Duarte & Denis Silva da Silveira

  2. Administrative Sciences Department, Federal University of Pernambuco, Recife, Brazil

    Charlie Silva Lopes & Denis Silva da Silveira

Authors
  1. Vinícius Brito
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rafael Duarte
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Charlie Silva Lopes
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Denis Silva da Silveira
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rafael Duarte .

Editor information

Editors and Affiliations

  1. Department of Information Technologies and Systems, University of Castilla-La Mancha, Ciudad Real, Spain

    Mario Piattini

  2. Departamento de Engenharia Informatica, University of Coimbra, Coimbra, Portugal

    Paulo Rupino da Cunha

  3. Department of Information Technologies and Systems, University of Castilla-La Mancha, Ciudad Real, Spain

    Ignacio García Rodríguez de Guzmán

  4. Department of Information Technologies and Systems, University of Castilla-La Mancha, Ciudad Real, Spain

    Ricardo Pérez-Castillo

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Brito, V., Duarte, R., Lopes, C.S., da Silveira, D.S. (2019). Understanding Process Models Using the Eye-Tracking: A Systematic Mapping. In: Piattini, M., Rupino da Cunha, P., García Rodríguez de Guzmán, I., Pérez-Castillo, R. (eds) Quality of Information and Communications Technology. QUATIC 2019. Communications in Computer and Information Science, vol 1010. Springer, Cham. https://doi.org/10.1007/978-3-030-29238-6_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-29238-6_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-29237-9

  • Online ISBN: 978-3-030-29238-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation