Skip to main content

Particle Swarm Optimization Method Based Consistency Checking in UML Class and Activity Diagrams

  • Conference paper
  • First Online:
Book cover Innovations in Bio-Inspired Computing and Applications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 424))

Abstract

Unified Modeling Language models are the de facto industry standard for object-oriented modeling of the static and dynamic aspects of software systems. To ensure software quality, it is essential to maintain consistency between the models. Inconsistencies among the diagrams of a model may result in serious faults which are hard to detect and may lead to project failure. Complex systems require large number of diagrams and hence detection of inconsistencies among the diagrams has a significant role during the design phase of software development. In this paper we describe a method for detection of inconsistencies among the class and activity diagrams using particle swarm optimization technique. Particle Swarm Optimization (PSO) is a soft computing technique that provides solutions to optimization problems by maximizing certain objectives in a complex search space. The PSO algorithm is applied to detect inconsistency and to optimize the consistency value of the attributes to ensure consistency. The application of PSO algorithm provides consistent, optimized diagrams that result in the generation of more accurate code.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Floreano., D, Mattiussi, C.: Bio-inspired Artificial Intelligence: Theories, Methods, and Technologies. MIT Press, Cambridge (2008)

    Google Scholar 

  2. Kennedy., J, Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, vol. IV, pp. 1942–1948 (1995)

    Google Scholar 

  3. Liu, Wn., Easterbrook, S., Mylopoulos, J.: Rule-based detection of inconsistency in uml models. In: Workshop on Consistency Problems in UML-Based Software Development, vol. 5 (2002)

    Google Scholar 

  4. Blanc, X., Mougenot, A., Mounier, I., Mens, T.: Incremental detection of model inconsistencies based on model operations. In: CAiSE, vol. 9, pp. 32–46 (2009)

    Google Scholar 

  5. Particle Swarm Optimization.: http://www.swarmintelligence.org

  6. Van Der Straeten., R., Mens, T., Simmonds, J., Jonckers, V.: Using description logic to maintain consistency between UML models. In: “UML”  2003-The Unified Modeling Language. Modeling Languages and Applications, pp. 326–340. Springer Berlin Heidelberg (2003)

    Google Scholar 

  7. O’Keeffe, M., O, Cinneide, M.: Towards automated design improvement through combinatorial optimization. In: Proceedings of Workshop on Directions in Software Engineering Environments (2004)

    Google Scholar 

  8. Blanc., X., Mounier, I., Mougenot, A., Mens, T.: Detecting model inconsistency through operation-based model construction. In: ACM/IEEE 30th International Conference on Software Engineering, 2008. ICSE’08, pp. 511–520. IEEE (2008)

    Google Scholar 

  9. Dubauskaite, R., Vasilecas, O.: Method on specifying consistency rules among different aspect models, expressed in UML. Elektronika ir Elektrotechnika 19(3), 77–81 (2013)

    Article  Google Scholar 

  10. Egyed., A.: Instant consistency checking for the UML. In: Proceedings of the 28th International Conference on Software Engineering, pp. 381–390. ACM (2006)

    Google Scholar 

  11. Van Der Straeten, R., Simmonds, J., Mens, T.: Detecting inconsistencies between UML models using description logic. Description Logics 81 (2003)

    Google Scholar 

  12. Saini, D.K., Sharma, Y.: Soft computing particle swarm optimization based approach for class responsibility assignment problem. Soft Comput. 40(12) (2012)

    Google Scholar 

  13. Ducatelle., F, Di Caro, G.A., Gambardella, L.M.: Principles and applications of swarm intelligence for adaptive routing in telecommunications networks. Swarm Intell. 4(3) 173–198 (2010)

    Google Scholar 

  14. Shamshiri, M., Gan, C.K., Mariana, Y., Ruddin, M., Ghani, A: Using particle swarm optimization algorithm in the distribution system planning. Aust. J. Basic Appl Sci 7(3), 85–92 (2013)

    Google Scholar 

  15. Revathi, C., Mythily, M.: A Uml/Marte detection of starvation and deadlocks at the design level in concurrent system. Int.J. Comput. Technol. Appl. 4(2), 279–285 (2013)

    Google Scholar 

  16. Nytun, J.P., Jensen, C.S.: Modeling and testing legacy data consistency requirements. In: “UML” 2003-The Unified Modeling Language. Modeling Languages and Applications, pp. 341–355. Springer, Berlin Heidelberg (2003)

    Google Scholar 

  17. Engels, G., Küster, J.M., Heckel, R., Groenewegen, L.: A methodology for specifying and analyzing consistency of object-oriented behavioral models. In: ACM SIGSOFT Software Engineering Notes, vol. 26, no. 5, pp. 186–195. ACM (2001)

    Google Scholar 

  18. Kennedy, J., Kennedy, J.F., Eberhart, R.C., Shi, Y.: Swarm Intelligence. Morgan Kaufmann (2001)

    Google Scholar 

  19. Peram, T., Veeramachaneni, K., Mohan, C.K.: Fitness-distance-ratio based particle swarm optimization. In: Swarm Intelligence Symposium, SIS’03, Proceedings of the 2003 IEEE, pp. 174–181. IEEE (2003)

    Google Scholar 

  20. Bai., Qinghai.: Analysis of particle swarm optimization algorithm. Computer and information science 3, no. 1, p. 180 (2010)

    Google Scholar 

  21. Hu, X., Eberhart, R.C.: Adaptive particle swarm optimization: detection and response to dynamic systems. In: WCCI, pp. 1666–1670. IEEE (2002)

    Google Scholar 

  22. De Souza, L.S., de Miranda, P.B.C., Prudencio, R.B.C., de Barros, F.: A multi-objective particle swarm optimization for test case selection based on functional requirements coverage and execution effort. In: 23rd IEEE International Conference on Tools with Artificial Intelligence (ICTAI), pp. 245–252. IEEE (2011)

    Google Scholar 

  23. Huzar, Z., Kuzniarz, L., Reggio, G., Sourrouille, J.L.: Consistency problems in UML-based software development. In: UML Modeling Languages and Applications, pp. 1–12. Springer Berlin, Heidelberg (2005)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Renu George .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

George, R., Samuel, P. (2016). Particle Swarm Optimization Method Based Consistency Checking in UML Class and Activity Diagrams. In: Snášel, V., Abraham, A., Krömer, P., Pant, M., Muda, A. (eds) Innovations in Bio-Inspired Computing and Applications. Advances in Intelligent Systems and Computing, vol 424. Springer, Cham. https://doi.org/10.1007/978-3-319-28031-8_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-28031-8_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28030-1

  • Online ISBN: 978-3-319-28031-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics