skip to main content
10.1145/1921705.1921710acmotherconferencesArticle/Chapter ViewAbstractPublication PagesecoopConference Proceedingsconference-collections
research-article

Reliability of transaction identification in use cases

Published:21 June 2010Publication History

ABSTRACT

Context: The concept of transaction is used in Use Case Points (UCP), and in many other functional size measurement methods, to capture the smallest unit of functionality that should be considered while measuring the size of a system. Unfortunately, in the case of the UCP method many different definitions of use-case transaction (and approaches to their identification) have been proposed thus far. Therefore, a question arises whether all of them define the same concept, and do they provide similar results when used by different people.

Objective: The goal of this study was to investigate differences and similarities between the existing definitions of use-case transactions that can have an impact on reliability of the use-case-based functional size measurement.

Method: A controlled experiment was conducted on a group of 120 students. The independent variable was a technique used for transaction identification (four methods were investigated), while the dependent variable was the number of transactions identified by participants in a use-case-based requirements specification.

Results: A significant difference in the median number of transactions was observed between groups using the original Karner's definition, and the definition proposed by Diev, which is based on the elementary process known from Function Point Analysis. In addition, a list of problems influencing intramethod reliability was defined based on the qualitative analysis of the experiment data.

Conclusions: It seems that there are two main sources of use-case transactions definitions. The Karner's definition of transaction is based on the concept of use-case transaction proposed by Jacobson -- the inventor of use cases, while the second one proposed by Diev, is based on the elementary process. There are also other methods for transaction identification that follow one of these definitions (e.g. Robiolo and Orosco stimuli-verbs approach). The important observation is that both main definitions of transactions yield different on-average results when used by different people. Therefore, it is important to consistently use only one in order to create a reliable historical database.

References

  1. S. Adolph, P. Bramble, A. Cockburn, and A. Pols. Patterns for Effective Use Cases. Addison-Wesley, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. A. Albrecht. Measuring Application Development Productivity. Proceedings of the Joint SHARE/GUIDE/IBM Application Development Symposium, pages 83--92, 1979.Google ScholarGoogle Scholar
  3. B. Alchimowicz, J. Jurkiewicz, M. Ochodek, and J. Nawrocki. Building Benchmarks for Use Cases. Computing and Informatics, 29(1):27--44, 2010.Google ScholarGoogle Scholar
  4. B. Anda. Comparing Effort Estimates Based on Use Case Points with Expert Estimates. Empirical Assessment in Software Engineering (EASE 2002), 2002.Google ScholarGoogle Scholar
  5. B. Anda, H. Dreiem, D. Sjøberg, and M. Jørgensen. Estimating Software Development Effort Based on Use Cases-Experiences from Industry. Fourth International Conference on the UML, pages 487--504, 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. A. Cockburn. Writing Effective Use Cases. Addison-Wesley Professional, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. R. Collaris and E. Dekker. Software cost estimation using use case points: Getting use case transactions straight. IBM, The Rational Edge, 2009.Google ScholarGoogle Scholar
  8. S. Diev. Software estimation in the maintenance context. ACM SIGSOFT Software Engineering Notes, 31(2):1--8, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. S. Diev. Use cases modeling and software estimation: Applying Use Case Points. ACM SIGSOFT Software Engineering Notes, 31(6):1--4, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. T. Fetcke, A. Abran, and T. Nguyen. Mapping the OO-Jacobson approach into function point analysis. Proceedings of TOOLS-23, 1997. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. D. Garmus and D. Herron. Function Point Analysis: Measurement Practices for Successful Software Projects. Addison-Wesley Boston, 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. M. Hollander and D. Wolfe. Nonparametric Statistical Methods. John Wiley and Sons, New York, 2nd edition, 1999.Google ScholarGoogle Scholar
  13. ISO/IEC. 20968:2002 Mk II Function Point Analysis - Counting Practices Manual, 2002.Google ScholarGoogle Scholar
  14. ISO/IEC. 19761:2003 COSMIC-FFP - A functional size measurement method, 2003.Google ScholarGoogle Scholar
  15. ISO/IEC. 20926:2003 IFPUG 4.1 Unadjusted functional size measurement method - Counting practices manual, 2003.Google ScholarGoogle Scholar
  16. I. Jacobson, M. Christerson, P. Jonsson, and G. Övergaard. Object-Oriented Software Engineering: A Use Case Driven Approach. Addison Wesley Longman, Inc., 1992. Google ScholarGoogle Scholar
  17. G. Karner. Resource Estimation for Objectory Projects. Objective Systems SF AB (copyright owned by Rational Software), 1993.Google ScholarGoogle Scholar
  18. B. Kitchenham, S. Pfleeger, and N. Fenton. Towards a Framework for Software Measurement Validation. IEEE Transactions on Software Engineering, 1995. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. W. Kruskal and W. Wallis. Use of ranks in one-criterion variance analysis. Journal of the American Statistical Association, 47(260):583--621, 1952.Google ScholarGoogle ScholarCross RefCross Ref
  20. S. Kusumoto, F. Matukawa, K. Inoue, S. Hanabusa, and Y. Maegawa. Estimating Effort by Use Case Points: Method, Tool and Case Study. Proceedings. 10th International Symposium on Software Metrics, pages 292--299, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. T. McCabe. A complexity measure. IEEE Transactions on software Engineering, pages 308--320, 1976. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. M. Ochodek and J. Nawrocki. Automatic Transactions Identification in Use Cases. In Balancing Agility and Formalism in Software Engineering: 2nd IFIP Central and East European Conference on Software Engineering Techniques CEE-SET 2007, volume 5082 of LNCS, pages 55--68. Springer Verlag, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. M. Ochodek and J. Nawrocki. Enhancing Use-Case-Based Effort Estimation with Transaction Types. Foundations of Computing and Decisions Sciences, 35(2), 2010.Google ScholarGoogle Scholar
  24. J. Ouwerkerk and A. Abran. An Evaluation of the Design of Use Case Points (UCP). In A. Abran, R. Dumke, and M. Ruiz, editors, Proceedings of the International Conference on Software Process and Product Measurement MENSURA 2006, pages 83--97. Publish Service of the University of Cádiz www.uca.es/publicaciones, 2006.Google ScholarGoogle Scholar
  25. G. Övergaard and K. Palmkvist. Use Cases: Patterns and Blueprints. Addison-Wesley, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. K. Ribu. Estimating object-oriented software projects with use cases. Master's thesis, University of Oslo, Department of Informatics, 2001.Google ScholarGoogle Scholar
  27. G. Robiolo and R. Orosco. Employing use cases to early estimate effort with simpler metrics. Innovations in Systems and Software Engineering, 4(1):31--43, 2008.Google ScholarGoogle ScholarCross RefCross Ref
  28. G. Schneider and J. P. Winters. Applying Use Cases: A Practical Guide. Addison-Wesley, 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. S. Shapiro and M. Wilk. An analysis of variance test for normality (complete samples). Biometrika, 52(3--4):591--611, 1965.Google ScholarGoogle Scholar
  30. C. Symons. Software sizing and estimating: Mk II FPA (Function Point Analysis). Wiley Series In Software Engineering Practice, 1991. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. K. E. Wiegers. Software Requirements. Microsoft Press, Redmond, WA, USA, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. M. Wilk and R. Gnanadesikan. Probability plotting methods for the analysis for the analysis of data. Biometrika, 55(1):1, 1968.Google ScholarGoogle Scholar

Index Terms

  1. Reliability of transaction identification in use cases

            Recommendations

            Comments

            Login options

            Check if you have access through your login credentials or your institution to get full access on this article.

            Sign in
            • Published in

              cover image ACM Other conferences
              FSM '10: Proceedings of the Workshop on Advances in Functional Size Measurement and Effort Estimation
              June 2010
              40 pages
              ISBN:9781450305396
              DOI:10.1145/1921705

              Copyright © 2010 ACM

              Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

              Publisher

              Association for Computing Machinery

              New York, NY, United States

              Publication History

              • Published: 21 June 2010

              Permissions

              Request permissions about this article.

              Request Permissions

              Check for updates

              Qualifiers

              • research-article
            • Article Metrics

              • Downloads (Last 12 months)2
              • Downloads (Last 6 weeks)0

              Other Metrics

            PDF Format

            View or Download as a PDF file.

            PDF

            eReader

            View online with eReader.

            eReader