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Agile development with software process mining

Published:26 May 2014Publication History

ABSTRACT

Modern companies continue investing more and more in the creation, maintenance and change of software systems, but the proper specification and design of such systems continues to be a challenge. The majority of current approaches either ignore real user and system runtime behavior or consider it only informally. This leads to a rather prescriptive top-down approach to software development.

In this paper, we propose a bottom-up approach, which takes event logs (e.g., trace data) of a software system for the analysis of the user and system runtime behavior and for improving the software. We use well-established methods from the area of process mining for this analysis. Moreover, we suggest embedding process mining into the agile development lifecycle.

The goal of this position paper is to motivate the need for foundational research in the area of software process mining (applying process mining to software analysis) by showing the relevance and listing open challenges. Our proposal is based on our experiences with analyzing a big productive touristic system. This system was developed using agile methods and process mining could be effectively integrated into the development lifecycle.

References

  1. S. Balsamo, A. D. Marco, P. Inverardi, and M. Simeoni. Model-based performance prediction in software development: A survey. IEEE Transactions on Software Engineering, 30(5):295–310, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. K. Beck, M. Beedle, A. van Bennekum, A. Cockburn, W. Cunningham, M. Fowler, J. Grenning, J. Highsmith, A. Hunt, R. Jeffries, J. Kern, B. Marick, R. C. Martin, S. Mellor, K. Schwaber, J. Sutherland, and D. Thomas. Manifesto for agile software development, 2001.Google ScholarGoogle Scholar
  3. P. Boldi, F. Bonchi, C. Castillo, D. Donato, A. Gionis, and S. Vigna. The query-flow graph: Model and applications. In Proceedings of the 17th ACM Conference on Information and Knowledge Management, CIKM ’08, pages 609–618, New York, NY, USA, 2008. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. J. Borges and M. Levene. Evaluating variable-length markov chain models for analysis of user web navigation sessions. IEEE Trans. on Knowl. and Data Eng., 19(4):441–452, Apr. 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. F. P. Brooks. The Design of Design: Essays from a Computer Scientist. Addison-Wesley Professional, 1st edition, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. F. Chierichetti, R. Kumar, P. Raghavan, and T. Sarlos. Are web users really markovian? In Proceedings of the 21st International Conference on World Wide Web, WWW ’12, pages 609–618, New York, NY, USA, 2012. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. J. Cook and A. Wolf. Discovering Models of Software Processes from Event-Based Data. ACM Transactions on Software Engineering and Methodology, 7(3):215–249, 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. B. Cornelissen, A. Zaidman, A. van Deursen, L. Moonen, and R. Koschke. A systematic survey of program comprehension through dynamic analysis. IEEE Trans. Softw. Eng., 35(5):684–702, Sept. 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. A. A. de Medeiros, A. Weijters, and W. van der Aalst. Genetic Process Mining: An Experimental Evaluation. Data Mining and Knowledge Discovery, 14(2):245–304, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. M. Hilbert and P. Lopez. The World’s Technological Capacity to Store, Communicate, and Compute Information. Science, 332(6025):60–65, 2011.Google ScholarGoogle ScholarCross RefCross Ref
  11. IEEE Task Force on Process Mining. Process Mining Manifesto. In F. Daniel, K. Barkaoui, and S. Dustdar, editors, Business Process Management Workshops, volume 99 of Lecture Notes in Business Information Processing, pages 169–194. Springer-Verlag, Berlin, 2012.Google ScholarGoogle Scholar
  12. E. Kindler, V. Rubin, and W. Schäfer. Activity Mining for Discovering Software Process Models. In B. Biel, M. Book, and V. Gruhn, editors, Proc. of the Software Engineering 2006 Conference, Leipzig, Germany, volume P-79 of LNI, pages 175–180. Gesellschaft für Informatik, Mar. 2006.Google ScholarGoogle Scholar
  13. R. Lencevicius, E. Metz, and A. Ran. Tracing execution of software for design coverage. In Proceedings of the 16th IEEE International Conference on Automated Software Engineering, ASE ’01, pages 328–, Washington, DC, USA, 2001. IEEE Computer Society. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. J. Manyika, M. Chui, B. Brown, J. Bughin, R. Dobbs, C. Roxburgh, and A. Byers. Big Data: The Next Frontier for Innovation, Competition, and Productivity. McKinsey Global Institute, 2011.Google ScholarGoogle Scholar
  15. B. Mobasher, R. Cooley, and J. Srivastava. Automatic personalization based on web usage mining. Commun. ACM, 43(8):142–151, Aug. 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. M. Peiris and J. H. Hill. Adapting system execution traces to support analysis of software system performance properties. J. Syst. Softw., 86(11):2849–2862, Nov. 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. V. Rubin, C. Günther, W. van der Aalst, E. Kindler, B. van Dongen, and W. Schäfer. Process Mining Framework for Software Processes. In Q. Wang, D. Pfahl, and D. Raffo, editors, International Conference on Software Process, Software Process Dynamics and Agility (ICSP 2007), volume 4470 of Lecture Notes in Computer Science, pages 169–181. Springer-Verlag, Berlin, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. C. Sauer, A. Gemino, and B. H. Reich. The Impact of Size and Volatility on IT Project Performance. Commun. ACM, 50(11):79–84, Nov. 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. K. Schwaber and M. Beedle. Agile Software Development with Scrum. Prentice Hall PTR, Upper Saddle River, NJ, USA, 1st edition, 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. The Standish Group. Chaos manifesto 2013. http://versionone.com/assets/img/files/ ChaosManifesto2013.pdf, 2013.Google ScholarGoogle Scholar
  21. W. van der Aalst. Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer-Verlag, Berlin, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. W. van der Aalst. Process Mining. Communications of the ACM, 55(8):76–83, 2012.Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. W. van der Aalst, V. Rubin, H. Verbeek, B. van Dongen, E. Kindler, and C. Günther. Process Mining: A Two-Step Approach to Balance Between Underfitting and Overfitting. Software and Systems Modeling, 9(1):87–111, 2010.Google ScholarGoogle ScholarCross RefCross Ref
  24. W. van der Aalst, A. Weijters, and L. Maruster. Workflow Mining: Discovering Process Models from Event Logs. IEEE Transactions on Knowledge and Data Engineering, 16(9):1128–1142, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. J. van der Werf, B. van Dongen, C. Hurkens, and A. Serebrenik. Process Discovery using Integer Linear Programming. Fundamenta Informaticae, 94:387–412, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. H. Verbeek, J. Buijs, B. van Dongen, and W. van der Aalst. ProM 6: The Process Mining Toolkit. In M. L. Rosa, editor, Proc. of BPM Demonstration Track 2010, volume 615 of CEUR Workshop Proceedings, pages 34–39, 2010.Google ScholarGoogle Scholar
  27. Y. Wang. Software Engineering Processes: Principles and Applications. CRC Press, April 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. E. Yourdon. Death March. Yourdon Press Series. Prentice Hall Professional Technical Reference, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library

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      • Published in

        cover image ACM Other conferences
        ICSSP 2014: Proceedings of the 2014 International Conference on Software and System Process
        May 2014
        199 pages
        ISBN:9781450327541
        DOI:10.1145/2600821

        Copyright © 2014 ACM

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        Publication History

        • Published: 26 May 2014

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