A Case Study to Enable and Monitor Real IT Companies Migrating from Waterfall to Agile

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8583)


Agile development methods are becoming increasingly important to face continuously changing requirements. Nevertheless, the adoption of such methods in industrial environments still needs to be fostered. Companies call for tools to keep under control both agility and coordination of IT teams.

In this paper, we report on an empirical case study aiming at enabling real companies migrating from Waterfall to Agile. Our research effort has been spent in introducing 11 different IT small and medium-sized enterprises to Agile, and to observe them executing projects. To have a common evaluation framework, we selected a set of 61 metrics, with the purpose of measuring the evolution towards Agile. We provide readers with empirical data on two categories of companies’ feedbacks: (i) the metrics they considered to be useful beyond the theoretical definitions; (ii) the tools they integrated with existing development environments to collect data from metrics, and evaluate quantitative improvements of Agile.


Agile Software Metrics Software Engineering Waterfall Migration towards Agile 


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  1. 1.
    Beck, K., Beedle, M., Bennekum, A., Van, C.A., Cunningham, W., Fowler, M., Grenning, J., Highsmith, J., Hunt, A., Jeffries, R., Kern, J., Marick, B., Martin, R.C., Mellor, S., Schwaber, K., Sutherland, J., Thomas, D.: Manifesto for Agile Software Development,
  2. 2.
    Misra, S., Omorodion, M.: Survey on agile metrics and their inter-relationship with other traditional development metrics. ACM SIGSOFT Softw. Eng. Notes 36, 1 (2011)CrossRefGoogle Scholar
  3. 3.
    Nguyen, V., Pham, V., Lam, V.: qEstimation: a process for estimating size and effort of software testing. In: Proceedings of the 2013 International Conference on Software and System Process, ICSSP 2013, p. 20. ACM Press, New York (2013)CrossRefGoogle Scholar
  4. 4.
    Nguyen, V., Pham, V., Lam, V.: Test Case Point Analysis: An Approach to Estimating Software Testing Size,
  5. 5.
    Martin, R.C.: Agile Software Development: Principles, Patterns, and Practices. Prentice Hall PTR (2003)Google Scholar
  6. 6.
    Dubinsky, Y., Talby, D., Hazzan, O., Keren, A.: Agile metrics at the Israeli Air Force. In: Agile Development Conference (ADC 2005), pp. 12–19. IEEE Comput. Soc. (2005)Google Scholar
  7. 7.
    Triacca, L., Bolchini, D., Botturi, L., Inversini, A.: MiLE: Systematic usability evaluation for e-learning web applications. In: Proceedings of World Conference on Educational Multimedia, Hypermedia and Telecommunications, Chesapeake, VA, pp. 4398–4405 (2004)Google Scholar
  8. 8.
    Zhu, H., Hall, P.A.V., May, J.H.R.: Software unit test coverage and adequacy. ACM Comput. Surv. 29, 366–427 (1997)CrossRefGoogle Scholar
  9. 9.
    Kunz, M., Dumke, R.R., Zenker, N.: Software Metrics for Agile Software Development, pp. 673–678 (2008)Google Scholar
  10. 10.
    Royce, W.: Pragmatic Quality Metrics for Evolutionary Software, TRW Space and Defence Sector, Redondo Beach, California (1990)Google Scholar
  11. 11.
    Khan, R.A., Mustafa, K., Ahson, S.I.: Software Quality: Concepts and Practices (2006)Google Scholar
  12. 12.
    Ganpati, A., Kalia, A., Singh, H.: A Comparative Study of Maintainability Index of Open Source Software. Int. J. Emerg. Technol. Adv. Eng. 2, 228–230 (2012)Google Scholar
  13. 13.
  14. 14.
    Royce, W.: Software Project Management: A Unified Framework (1998)Google Scholar
  15. 15.
    Laanti, M., Salo, O., Abrahamsson, P.: Agile methods rapidly replacing traditional methods at Nokia: A survey of opinions on agile transformation. Inf. Softw. Technol. 53, 276–290 (2011)CrossRefGoogle Scholar
  16. 16.
    Ji, F., Sedano, T.: Comparing extreme programming and Waterfall project results. In: 24th IEEE-CS Conference on Software Engineering Education and Training (CSEE & T), pp. 482–486. IEEE (2011)Google Scholar
  17. 17.
    Hassan, A.E., Xie, T.: Mining software engineering data. In: Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering, ICSE 2010, p. 503. ACM Press, New York (2010)Google Scholar
  18. 18.
    Williams, C., Hollingsworth, J.: Automatic mining of source code repositories to improve bug finding techniques. IEEE Trans. Softw. Eng. 31, 466–480 (2005)CrossRefGoogle Scholar
  19. 19.
    Poncin, W., Serebrenik, A., Van Den Brand, M.: Process Mining Software Repositories. In: 15th European Conference on Software Maintenance and Reengineering, pp. 5–14. IEEE (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Innovation EngineeringUniversity of SalentoLecceItaly

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