Bi-level Identification of Web Service Defects

  • Hanzhang Wang
  • Marouane KessentiniEmail author
  • Ali Ouni
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9936)


Successful Web services must evolve to remain relevant (e.g. requirements update, bugs fix, etc.), but this process of evolution increases complexity and can cause the Web service interface design to decay and lead to significantly reduced usability and popularity of the services. Maintaining a high level of design quality is extremely expensive due to monetary and time pressures that force programmers to neglect improving the quality of their interfaces. A more fundamental reason is that there is little support to automatically identify design defects at the Web service interface level and reduce the high calibration effort to determine manually the threshold value for each quality metric to identify design defects. In this paper, we propose to treat the generation of interface design defects detection rules as a bi-level optimization problem. To this end, the upper level problem generates a set of detection rules, as combination of quality metrics, which maximizes the coverage of a base of defects examples extracted from several Web services and artificial defects generated by the lower level. The lower level maximizes the number of generated artificial defects that cannot be detected by the rules produced by the upper level. The statistical analysis of our experiments over 30 runs on a benchmark of 415 Web services shows that 8 types of Web service defects were detected with an average of more than 93 % of precision and 98 % recall. The results confirm the outperformance of our bi-level proposal compared to state-of-art Web service design defects detection techniques and the survey performed by potential users and programmers also shows the relevance of the detected defects.


Web service interface Design defects Quality of services 


  1. 1.
    Dudney, B., Krozak, J.K., Asbury, S., Osborne, D.: J2EE Antipatterns, 1st edn. Wiley, Hoboken (2003)Google Scholar
  2. 2.
    Rotem-Gal-Oz, A., Bruno, E., Dahan, U.: SOA Patterns, pp. 38–62. Manning Publications, Greenwich (2012)Google Scholar
  3. 3.
    Král, J., Žemlicka, M., Popular SOA antipatterns. In: Future Computing, Service Computation, Cognitive, Adaptive, Content, Patterns, pp. 271–276. IEEE (2009)Google Scholar
  4. 4.
    Moha, N., Palma, F., Nayrolles, M., Conseil, B.J., Guéhéneuc, Y.-G., Baudry, B., Jézéquel, J.-M.: Specification and detection of SOA antipatterns. In: Liu, C., Ludwig, H., Toumani, F., Yu, Q. (eds.) Service Oriented Computing. LNCS, vol. 7636, pp. 1–16. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  5. 5.
    Palma, F., Moha, N., Tremblay, G., Guéhéneuc, Y.-G.: Specification and detection of SOA antipatterns in web services. In: Avgeriou, P., Zdun, U. (eds.) ECSA 2014. LNCS, vol. 8627, pp. 58–73. Springer, Heidelberg (2014)Google Scholar
  6. 6.
    Bard, J.: Practical Bilevel Optimization: Algorithms and Applications, vol. 30. Kluwer, Dordrecht (1998)zbMATHGoogle Scholar
  7. 7.
    Giri, B.K., Hakanen, J., Miettinen, K., Chakraborti, N.: Genetic programming through bi-objective genetic algorithms with a study of a simulated moving bed process involving multiple objectives. Appl. Soft Comput. 13(5), 2613–2623 (2013)CrossRefGoogle Scholar
  8. 8.
    Ouni, A., Kessentini, M., Inoue, K.: Search-based web service antipatterns detection. In: IEEE Transactions on Services Computing, pp. 1–21. IEEE (2016, to appear)Google Scholar
  9. 9.
    Frakes, W.B., Baeza-Yates, R. (eds.): Information Retrieval: Data Structures and Algorithms. Prentice-Hall, Inc., Upper Saddle River (1992)Google Scholar
  10. 10.
    Rodriguez, J.M., Crasso, M., Mateos, C., Zunino, A.: Best practices for describing, consuming, and discovering web services: a comprehensive toolset. Softw. Pract. Experience 43(6), 613–639 (2013)CrossRefGoogle Scholar
  11. 11.
    Rodriguez, J.M., Crasso, M., Zunino, A., Campo, M.: Automatically detecting opportunities for web service descriptions improvement. In: Cellary, W., Estevez, E. (eds.) Software Services for e-World. IFIP AICT, vol. 341, pp. 139–150. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  12. 12.
    Ankur, S., Pekka, M., Anton, F., Kalyanmoy, D.: Multi-objective Stackelberg game between a regulating authority and a mining company: a case study in environmental economics. In: IEEE Congress on Evolutionary Computation, Cancun, Mexico, pp. 478–485 (2013)Google Scholar
  13. 13.
    Mateos, C., Zunino, A., Coscia, J.L.O.: Avoiding WSDL bad practices in code-first web services. SADIO Electron. J. Inf. Oper. Res. 11(1), 31–48 (2012)Google Scholar
  14. 14.
    Torkamani, M.A., Bagheri, H.: A systematic method for identification of anti-patterns in service oriented system development. Int. J. Electr. Comput. Eng. 4(1), 16–23 (2014)Google Scholar
  15. 15.
    Mateos, C., Rodriguez, J.M., Zunino, A.: A tool to improve code-first web services discoverability through text mining techniques. Softw. Pract. Experience 45(7), 925–948 (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Computer and Information Science DepartmentUniversity of MichiganAnn ArborUSA
  2. 2.Graduate School of Information Science and TechnologyOsaka UniversitySuitaJapan

Personalised recommendations