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Applying Strategies to Recommend Groupware Tools According to Cognitive Characteristics of a Team

  • Gabriela N. Aranda
  • Aurora Vizcaíno
  • Alejandra Cechich
  • Mario Piattini
Part of the Studies in Computational Intelligence book series (SCI, volume 323)

Abstract

Global Software Development (GSD) projects became a common way to develop software during the last decade. However, despite the economic benefits that globalization may introduce, GSD faces a series of factors that affect communication and challenge its success. In order to improve communication in such environments, we focus on techniques from the field of cognitive psychology to define a new approach to groupware tools selection. In this paper we present a series of strategies to find the best choice for a given group of people, taking into account the different combinations of cognitive profiles that can arise in a GSD project, as well as the application of one of these strategies in a case study.

Keywords

Cognitive informatics requirements elicitation global software development projects 

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References

  1. 1.
    Aranda, G., Cechich, A., Vizcaíno, A., Castro-Schez, J.J.: Using fuzzy sets to analyse personal preferences on groupware tools. In: Proc. X Congreso Argentino de Ciencias de la Computación, CACIC 2004, San Justo, Argentina, pp. 549–560 (October 2004)Google Scholar
  2. 2.
    Aranda, G., Vizcaíno, A., Cechich, A., Piattini, M.: A Cognitive-Based Approach to Improve Distributed Requirement Elicitation Processes. In: Proc. 4th IEEE International Conference on Cognitive Informatics (ICCI 2005), Irvine, USA, pp. 322–330 (August 2005)Google Scholar
  3. 3.
    Aranda, G., Vizcaíno, A., Cechich, A., Piattini, M.: How to choose groupware tools considering stakeholders’ preferences during requirements elicitation? In: Haake, J.M., Ochoa, S.F., Cechich, A. (eds.) CRIWG 2007. LNCS, vol. 4715, pp. 319–327. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  4. 4.
    Aranda, G., Vizcaíno, A., Cechich, A., Piattini, M., Castro-Schez, J.J.: Cognitive-Based Rules as a Means to Select Suitable Groupware Tools. In: Proc. 5th IEEE International Conference on Cognitive Informatics (ICCI 2006), Beijing, China, July 2006, pp. 418–423 (2006)Google Scholar
  5. 5.
    Blank, G.D., Roy, S., Sahasrabudhe, S., Pottenger, W.M., Kessler, G.D.: Adapting Multimedia for Diverse Student Learning Styles. Journal of Computing in Small Colleges 18, 45–58 (2003)Google Scholar
  6. 6.
    Bostrom, R.P., Olfman, L., Sein, M.K.: The Importance of Individual Differences in End-User Training: The Case for Learning Style. In: Proc. 1988 ACM SIGCPR Conference, Maryland, pp. 133–141 (April 1988)Google Scholar
  7. 7.
    Castro, J.L., Castro-Schez, J.J., Zurita, J.M.: Learning Maximal Structure Rules in Fuzzy Logic for Knowledge Acquisition in Expert Systems. Fuzzy Sets and Systems 101, 331–342 (1999)zbMATHMathSciNetCrossRefGoogle Scholar
  8. 8.
    Chiew, V., Wang, Y.: From Cognitive Psychology to Cognitive Informatics. In: Proc. Second IEEE International Conference on Cognitive Informatics, ICCI 2003, London, UK, pp. 114–120 (August 2003)Google Scholar
  9. 9.
    Damian, D., Zowghi, D.: The impact of stakeholders geographical distribution on managing requirements in a multi-site organization. In: Proc. IEEE Joint International Conference on Requirements Engineering, RE 2002, Essen, Germany, pp. 319–328 (September 2002)Google Scholar
  10. 10.
    Davis, A.: Software Requirements: Objects, Functions and States. Prentice Hall, Upper Saddle River (1993)zbMATHGoogle Scholar
  11. 11.
    Felder, R.: Matters of Styles. ASEE Prism 6, 18–23 (1996)Google Scholar
  12. 12.
    Felder, R., Silverman, L.: Learning and Teaching Styles in Engineering Education. Engineering Education 78, 674–681 (1988 (and author preface written in 2002))Google Scholar
  13. 13.
    Felder, R., Spurlin, J.: Applications, Reliability and Validity of the Index of Learning Styles. International Journal of Engineering Education 21, 103–112 (2005)Google Scholar
  14. 14.
    Herbsleb, J.D.: Global Software Engineering: The Future of Socio-technical Coordination. In: Proc. International Conference on Software Engineering: Future of Software Engineering FOSE 2007, pp. 188–198. IEEE Computer Society, Minneapolis (2007)Google Scholar
  15. 15.
    Hickey, A.M., Davis, A.: Elicitation Technique Selection: How do experts do it? In: Proc. International Joint Conference on Requirements Engineering (RE 2003), pp. 169–178. IEEE Computer Society Press, Los Alamitos (2003)Google Scholar
  16. 16.
    Hickey, A.M., Davis, A.: Requirements Elicitation and Elicitation Technique Selection: A Model for Two Knowledge-Intensive Software Development Processes. In: Proc. 36th Annual Hawaii International Conference on Systems Sciences (HICSS), pp. 96–105 (January 2003)Google Scholar
  17. 17.
    Johansen, R.: Groupware: Computer Support for Business Teams. T.F. Press, New York (1988)Google Scholar
  18. 18.
    Lloyd, W., Rosson, M.B., Arthur, J.: Effectiveness of Elicitation Techniques in Distributed Requirements Engineering. In: Proc. 10th Anniversary IEEE Joint International Conference on Requirements Engineering, RE 2002, Essen, Germany, pp. 311–318 (September 2002)Google Scholar
  19. 19.
    Loucopoulos, P., Karakostas, V.: System Requirements Engineering. International Series in Software Engineering. Mc Graw-Hill, New York (1995)Google Scholar
  20. 20.
    Martín, A., Martínez, C., Martínez Carod, N., Aranda, G., Cechich, A.: Classifying Groupware Tools to Improve Communication in Geographically Distributed Elicitation. In: Proc. IX Congreso Argentino de Ciencias de la Computación, CACIC 2003, La Plata, Argentina, pp. 942–953 (October 2003)Google Scholar
  21. 21.
    Miller, J., Yin, Z.: A Cognitive-Based Mechanism for Constructing Software Inspection Teams. IEEE Transactions on Software Engineering 30(11), 811–825 (2004)CrossRefGoogle Scholar
  22. 22.
    Moallem, M.: The Implications of Research Literature on Learning Styles for the Design and Development of a Web-Based Course. In: Proc. International Conference on Computers in Education, ICCE 2002. Auckland, New Zealand, pp. 71–74 (2002)Google Scholar
  23. 23.
    Thomas, L., Ratcliffe, M., Woodbury, J., Jarman, E.: Learning styles and performance in the introductory programming sequence. In: Proc. 33rd SIGCSE Technical Symposium on Computer Science Education, Cincinnati, Kentucky, USA, pp. 33–37 (February 2002)Google Scholar
  24. 24.
    Wang, Y.: On Cognitive Informatics. In: Proc. First IEEE International Conference on Cognitive Informatics, ICCI 2002, Alberta, Canada, pp. 34–42 (August 2002)Google Scholar
  25. 25.
    Wu, C.C., Dale, N.B., Bethel, L.J.: Conceptual Models and Cognitive Learning Styles in Teaching Recursion. In: Proc. Twenty-ninth SIGCSE Technical Symposium on Computer Science Education, Atlanta, Georgia, United States, pp. 292–296 (February 1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Gabriela N. Aranda
    • 1
  • Aurora Vizcaíno
    • 2
  • Alejandra Cechich
    • 1
  • Mario Piattini
    • 2
  1. 1.GIISCo Research Group, Computing Sciences DepartmentUniversidad Nacional del ComahueNeuquénArgentina
  2. 2.ALARCOS Research Group, Information Systems and Technologies Department, UCLM-INDRA Research and Development InstituteUniversidad de Castilla-La ManchaCiudad RealSpain

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