Maintaining and Analyzing Production Process Definitions Using a Tree-Based Similarity Measure

  • Reinhard Stumptner
  • Christian Lettner
  • Bernhard Freudenthaler
  • Josef Pichler
  • Wilhelm Kirchmayr
  • Ewald Draxler
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9343)

Abstract

In this work a Case-Based reasoning system for managing production processes, declarative production process definitions in particular, with main focus on analysis and maintenance is introduced whereby each process task is represented by a case. A single process task definition includes among other elements, formulas, represented by fragmental program code. To get a meaningful similarity function among such cases, a new fuzzy tree edit distance metric on the formulas’ abstract syntax tree has been developed. The fuzzy tree edit distance addresses two aspects of similarity – similarity in terms of similar structure and similarity in terms of similar wording. As such, the proposed method represents a multidisciplinary approach to production process maintenance that includes methods from Case-Based reasoning and code clone detection.

Keywords

Case base maintenance Similarity measure Tree edit distance Code clone detection Abstract syntax tree Hierarchical clustering 
This is a preview of subscription content, log in to check access.

Notes

Acknowledgements

This work has been supported by the COMET-Program of the Austrian Research Promotion Agency (FFG)

References

  1. 1.
    Bach, K. et al.: Knowledge modeling with the open source tool myCBR. In: Proceedings of 10th Workshop on Knowledge Engineering and Software Engineering (KESE10) Co-located with 21st European Conference on Artificial Intelligence (ECAI 2014), Prague, Czech Republic, 19 August 2014Google Scholar
  2. 2.
    Baxter, I.D., et al.: Clone detection using abstract syntax trees. In: Proceedings., International Conference on Software Maintenance, 1998, pp. 368–377. IEEE (1998)Google Scholar
  3. 3.
    Bellon, S., et al.: Comparison and evaluation of clone detection tools. IEEE Trans. Softw. Eng. 33(9), 577–591 (2007)CrossRefGoogle Scholar
  4. 4.
    Bunke, H., Messmer, B.T.: Similarity measures for structured representations. In: Wess, Stefan, Richter, M., Althoff, Klaus-Dieter (eds.) EWCBR 1993. LNCS, vol. 837. Springer, Heidelberg (1994)CrossRefGoogle Scholar
  5. 5.
    Jalali, V., Leake, D.: Adaptation-Guided case base maintenance. In: Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, July 27–31, 2014, pp. 1875–1881. Québec City (2014)Google Scholar
  6. 6.
    Jiang, L., et al.: Deckard: scalable and accurate tree-based detection of code clones. In: Proceedings of the 29th International Conference on Software Engineering, pp. 96–105. IEEE Computer Society (2007)Google Scholar
  7. 7.
    Kodhai, E., Kanmani, S.: Method-level code clone detection through LWH (Light Weight Hybrid) approach. J. Softw. Eng. Res. Dev. 2(1), 1–29 (2014)CrossRefGoogle Scholar
  8. 8.
    Levenshtein, V.I.: Binary codes capable of correcting deletions, insertions and reversals. Sov. Phys. Dokl. 10, 707 (1966)MathSciNetMATHGoogle Scholar
  9. 9.
    Marianov, V., Serra, D.: Median problems in networks. Available at SSRN: 1428362 (2009)Google Scholar
  10. 10.
    Mayrand, J., et al.: Experiment on the automatic detection of function clones in a software system using metrics. In: Proceedings of International Conference on Software Maintenance 1996, pp. 244–253. IEEE (1996)Google Scholar
  11. 11.
    Pan, R., et al.: Mining competent case bases for case-based reasoning. Artif. Intell. 171(16–17), 1039–1068 (2007)MathSciNetCrossRefMATHGoogle Scholar
  12. 12.
    Pawlik, M., Augsten, N.: RTED: a robust algorithm for the tree edit distance. Proc. VLDB Endow. 5(4), 334–345 (2011)CrossRefGoogle Scholar
  13. 13.
    Reichert, M., Weber, B.: Enabling Flexibility in Process-Aware Information Systems: Challenges, Methods. Technologies. Springer, Heidelberg (2012)CrossRefMATHGoogle Scholar
  14. 14.
    Rosa, M.L., et al.: Business process variability modeling : a survey. ACM Comput. Surv. (2013)Google Scholar
  15. 15.
    Roth-Berghofer, T., et al.: Building case-based reasoning applications with myCBR and COLIBRI studio. In: Proceedings of the UKCBR 2012 Workshop on Case-Based Reasoning, pp. 71–82 (2012)Google Scholar
  16. 16.
    Roy, C.K., Cordy, J.R.: NICAD: accurate detection of near-miss intentional clones using flexible pretty-printing and code normalization. In: The 16th IEEE International Conference on Program Comprehension, 2008 (ICPC 2008), pp. 172–181. IEEE (2008)Google Scholar
  17. 17.
    Smiti, A., Elouedi, Z.: Article: overview of maintenance for case based reasoning systems. Int. J. Comput. Appl. 32(2), 49–56 (2011)Google Scholar
  18. 18.
    Smyth, B.: Case-base maintenance. In: Mira, J., Moonis, A., de Pobil, A.P. (eds.) IEA/AIE 1998. LNCS, vol. 1416. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  19. 19.
    Smyth, B., McKenna, E.: Modelling the competence of case-bases. In: Smyth, B., Cunningham, P. (eds.) EWCBR 1998. LNCS (LNAI), vol. 1488, pp. 208–220. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  20. 20.
    Stahl, A., Roth-Berghofer, T.R.: Rapid prototyping of CBR applications with the open source tool myCBR. In: Althoff, K.-D., Bergmann, R., Minor, M., Hanft, A. (eds.) ECCBR 2008. LNCS (LNAI), vol. 5239, pp. 615–629. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  21. 21.
    Wilson, D.C., Leake, D.B.: Maintaining case-based reasoners: dimensions and directions. Comput. Intell. 17(2), 196–213 (2001)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Reinhard Stumptner
    • 1
  • Christian Lettner
    • 1
  • Bernhard Freudenthaler
    • 1
  • Josef Pichler
    • 1
  • Wilhelm Kirchmayr
    • 2
  • Ewald Draxler
    • 2
  1. 1.Software Competence Center Hagenberg GmbHHagenbergAustria
  2. 2.Voestalpine Stahl GmbHLinzAustria

Personalised recommendations