Skip to main content

Modelling Software Tasks for Supporting Resource-Driven Adaptation

  • 138 Accesses

Part of the Lecture Notes in Business Information Processing book series (LNBIP,volume 487)

Abstract

Software systems execute tasks that depend on different types of resources. The variability of resources hinders the ability of software systems to execute important tasks. For example, in automated warehouses, malfunctioning robots could delay product deliveries and cause financial losses due to customer dissatisfaction. Resource-driven adaptation addresses the negative implications of resource variability. Hence, this paper presents a task modelling notation called SERIES, which is used for representing task models that support resource-driven adaptation in software systems. SERIES is complemented by a tool that enables software practitioners to create and modify task models. SERIES was evaluated through a study with software practitioners. The participants of this study were asked to explain and create task models and then provide their feedback on the usability of SERIES and the clarity of its semantic constructs. The results showed a very good user performance in explaining and creating task models using SERIES. These results were reflected in the feedback of the participants and the activities that they performed using SERIES.

Keywords

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   79.99
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

Learn about institutional subscriptions

References

  1. Cheng, B.H.C., et al.: Software engineering for self-adaptive systems: a research roadmap. In: Cheng, B.H.C., de Lemos, R., Giese, H., Inverardi, P., Magee, J. (eds.) Software Engineering for Self-Adaptive Systems. LNCS, vol. 5525, pp. 1–26. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-02161-9_1

    Chapter  Google Scholar 

  2. de Lemos, R., et al.: Software engineering for self-adaptive systems: a second research roadmap. In: de Lemos, R., Giese, H., Müller, H.A., Shaw, M. (eds.) Software Engineering for Self-Adaptive Systems II. LNCS, vol. 7475, pp. 1–32. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-35813-5_1

    Chapter  Google Scholar 

  3. Sousa, J.P., Poladian, V., Garlan, D., Schmerl, B., Shaw, M.: Task-based adaptation for ubiquitous computing. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 36(3), 328–340 (2006). https://doi.org/10.1109/TSMCC.2006.871588

    Article  Google Scholar 

  4. Perttunen, M., Jurmu, M., Riekki, J.: A QoS model for task-based service composition. In: Proceedings of the 4th International Workshop on Managing Ubiquitous Communications and Services, p. 11 (2007)

    Google Scholar 

  5. Paterno, F., Mancini, C., Meniconi, S.: ConcurTaskTrees: a diagrammatic notation for specifying task models. In: Howard, S., Hammond, J., Lindgaard, G. (eds.) INTERACT 1997. ITIFIP, pp. 362–369. Springer, Boston (1997). https://doi.org/10.1007/978-0-387-35175-9_58

    Chapter  Google Scholar 

  6. Martinie, C., Palanque, P., Winckler, M.: Structuring and composition mechanisms to address scalability issues in task models. In: Campos, P., Graham, N., Jorge, J., Nunes, N., Palanque, P., Winckler, M. (eds.) INTERACT 2011. LNCS, vol. 6948, pp. 589–609. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23765-2_40

    Chapter  Google Scholar 

  7. Xu, M., Buyya, R.: Brownout approach for adaptive management of resources and applications in cloud computing systems: a taxonomy and future directions. ACM Comput. Surv. (CSUR). 52, 1–27 (2019)

    Article  Google Scholar 

  8. Klein, C., Maggio, M., Årzén, K.-E., Hernández-Rodriguez, F.: Brownout: building more robust cloud applications. In: Proceedings of the 36th International Conference on Software Engineering - ICSE 2014, Hyderabad, India, pp. 700–711. ACM Press (2014)

    Google Scholar 

  9. Gotz, S., Gerostathopoulos, I., Krikava, F., Shahzada, A., Spalazzese, R.: Adaptive exchange of distributed partial Models@run.time for highly dynamic systems. In: 2015 IEEE/ACM 10th International Symposium on Software Engineering for Adaptive and Self-managing Systems, Florence, Italy, pp. 64–70. IEEE (2015)

    Google Scholar 

  10. Viswanathan, L., Jindal, A., Karanasos, K.: Query and resource optimization: bridging the gap. In: 2018 IEEE 34th International Conference on Data Engineering (ICDE), pp. 1384–1387. IEEE (2018)

    Google Scholar 

  11. Keeney, J., Cahill, V.: Chisel: a policy-driven, context-aware, dynamic adaptation framework. In: Proceedings POLICY 2003. IEEE 4th International Workshop on Policies for Distributed Systems and Networks, pp. 3–14. IEEE (2003)

    Google Scholar 

  12. Efstratiou, C., Friday, A., Davies, N., Cheverst, K.: A platform supporting coordinated adaptation in mobile systems. In: Proceedings Fourth IEEE Workshop on Mobile Computing Systems and Applications, pp. 128–137. IEEE (2002)

    Google Scholar 

  13. Christi, A., Groce, A., Gopinath, R.: Resource adaptation via test-based software minimization. In: 2017 IEEE 11th International Conference on Self-adaptive and Self-organizing Systems (SASO), pp. 61–70. IEEE (2017)

    Google Scholar 

  14. Christi, A., Groce, A.: Target selection for test-based resource adaptation. In: International Conference on Software Quality, Reliability and Security, pp. 458–469. IEEE (2018)

    Google Scholar 

  15. Fowler, M.: UML Distilled: A Brief Guide to the Standard Object Modeling Language. Addison-Wesley, Boston (2003)

    Google Scholar 

  16. Akiki, P., Zisman, A., Bennaceur, A.: SERIES: a task modelling notation for resource-driven adaptation. In: Proceedings of the 24th International Conference on Enterprise Information Systems, pp. 29–39. SCITEPRESS - Science and Technology Publications, Online Streaming (2022). https://doi.org/10.5220/0011001800003179

  17. Green, T.R.G., Petre, M.: Usability analysis of visual programming environments: a ‘cognitive dimensions’ framework. J. Vis. Lang. Comput. 7, 131–174 (1996)

    Article  Google Scholar 

  18. Akiki, P.A., Zisman, A., Bennaceur, A.: Work with what you’ve got: an approach for resource-driven adaptation. In: 2021 IEEE International Conference on Autonomic Computing and Self-organizing Systems Companion (ACSOS-C), DC, USA, pp. 105–110. IEEE (2021). https://doi.org/10.1109/ACSOS-C52956.2021.00030

  19. Calvary, G., Coutaz, J., Thevenin, D., Limbourg, Q., Bouillon, L., Vanderdonckt, J.: A unifying reference framework for multi-target user interfaces. Interact. Comput. 15, 289–308 (2003)

    Article  Google Scholar 

  20. Vidani, A.C., Chittaro, L.: Using a task modeling formalism in the design of serious games for emergency medical procedures. In: 2009 Conference in Games and Virtual Worlds for Serious Applications, pp. 95–102. IEEE (2009)

    Google Scholar 

  21. 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 

  22. Guerrero-García, J., González-Calleros, J., Vanderdonckt, J.: A comparative analysis of task modeling notations. Acta Universitaria 22, 90–97 (2012)

    Article  Google Scholar 

  23. Limbourg, Q., Vanderdonckt, J.: Comparing task models for user interface design. Handb. Task Anal. Hum.-Comput. Interact. 6, 135–154 (2004)

    Google Scholar 

  24. Martinie, C., Palanque, P., Bouzekri, E., Cockburn, A., Canny, A., Barboni, E.: Analysing and demonstrating tool-supported customizable task notations. Proc. ACM Hum.-Comput. Interact. 3, 1–26 (2019)

    Article  Google Scholar 

  25. Hartson, H.R., Gray, P.D.: Temporal aspects of tasks in the user action notation. Hum.-Comput. Interact. 7, 1–45 (1992)

    Article  Google Scholar 

  26. Kieras, D.: GOMS models for task analysis. In: Diaper, D., Stanton, N.A. (eds.) The Handbook of Task Analysis for Human-Computer Interaction (2004)

    Google Scholar 

  27. Giese, M., Mistrzyk, T., Pfau, A., Szwillus, G., Detten, M.: AMBOSS: a task modeling approach for safety-critical systems. In: Forbrig, P., Paternò, F. (eds.) Engineering Interactive Systems 2008, pp. 98–109. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-85992-5_8

    Chapter  Google Scholar 

  28. Annett, J.: Hierarchical task analysis. Handb. Cogn. Task Des. 2, 17–35 (2003)

    Article  Google Scholar 

  29. Van Der Veer, G.C., Lenting, B.F., Bergevoet, B.A.: GTA: groupware task analysis—modeling complexity. Acta Physiol. (Oxf) 91, 297–322 (1996)

    Google Scholar 

  30. Tarby, J.-C., Barthet, M.-F.: The DIANE+ method. In: CADUI, pp. 95–119 (1996)

    Google Scholar 

  31. Johnson, H., Hyde, J.: Towards modeling individual and collaborative construction of jigsaws using task knowledge structures (TKS). ACM Trans. Comput.-Hum. Interact. (TOCHI) 10, 339–387 (2003)

    Article  Google Scholar 

  32. Limbourg, Q., Vanderdonckt, J., Michotte, B., Bouillon, L., López-Jaquero, V.: USIXML: a language supporting multi-path development of user interfaces. In: Bastide, R., Palanque, P., Roth, J. (eds.) DSV-IS 2004. LNCS, vol. 3425, pp. 200–220. Springer, Heidelberg (2005). https://doi.org/10.1007/11431879_12

    Chapter  Google Scholar 

  33. Batra, D., Hoffler, J.A., Bostrom, R.P.: Comparing representations with relational and EER models. Commun. ACM 33, 126–139 (1990). https://doi.org/10.1145/75577.75579

    Article  Google Scholar 

  34. Shoval, P., Shiran, S.: Entity-relationship and object-oriented data modeling — an experimental comparison of design quality. Data Knowl. Eng. 21, 297–315 (1997). https://doi.org/10.1016/S0169-023X(97)88935-5

    Article  MATH  Google Scholar 

  35. Bork, D., Roelens, B.: A technique for evaluating and improving the semantic transparency of modeling language notations. Softw. Syst. Model. 20(4), 939–963 (2021). https://doi.org/10.1007/s10270-021-00895-w

    Article  Google Scholar 

  36. Benedek, J., Miner, T.: Measuring desirability: new methods for evaluating desirability in a usability lab setting. Proc. Usability Professionals Assoc. 2003, 57 (2002)

    Google Scholar 

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

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the Engineering and Physical Sciences Research Council [grant numbers EP/V026747/1, EP/R013144/1].

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Paul A. Akiki .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Akiki, P.A., Zisman, A., Bennaceur, A. (2023). Modelling Software Tasks for Supporting Resource-Driven Adaptation. In: Filipe, J., Śmiałek, M., Brodsky, A., Hammoudi, S. (eds) Enterprise Information Systems. ICEIS 2022. Lecture Notes in Business Information Processing, vol 487. Springer, Cham. https://doi.org/10.1007/978-3-031-39386-0_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-39386-0_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-39385-3

  • Online ISBN: 978-3-031-39386-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics