Abstract
Web services are being embraced by IT industry in the recent past to enable rapid development of distributed systems with optimal cost. Web services in SOA are self-adaptable to context, which makes SOA widely recognized in IT system as the technology, which has the potential of improving the receptiveness of both business and IT organizations. Web services help in building Service Based Systems (SBS) like Paytm, Amazon, Paypal, e-bay etc. which evolves frequently to fit the new user requirements which impacts the evolvability and quality of software design. Similar to software systems built using other paradigms, Service based systems also suffer from bad or poor design choices as in anti-pattern, code smells etc. Anti-patterns are explicit structures in the design that indicates violation of fundamental design principles and negatively impact the design quality. Anti-patterns have obstructive influence on the maintainability and perception of software systems. Thus there is a rising need for the early prediction of anti-patterns and refactoring them to improve the software quality in terms of execution cost, maintenance cost and memory consumption. In this work, a frame work is proposed for significant feature selection from source code metrics which includes Wilcoxon signed rank test, Univariate logistic regression analysis and Cross-correlation analysis. Then the different sets of features from various steps along with the original source code metrics are considered and are used for anti-pattern detection using 13 machine learning algorithms. Experimental results show the approximation capability of different classifiers and data balancing techniques with the features selected from the various steps of feature validation framework in addition to the original features for developing anti-pattern prediction model. The results also shows that the prediction model built with by the ensemble techniques using the features obtained from proposed feature selection framework outperforms other techniques.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Král, J., Zemlicka, M.: Crucial service-oriented antipatterns, vol. 2, pp. 160–171. International Academy, Research and Industry Association (IARIA) (2008)
Brown, W.H., Malveau, R.C., McCormick, H.W., Mowbray, T.J.: AntiPatterns: Refactoring Software, Architectures, and Projects in Crisis. Wiley, Hoboken (1998)
Dudney, B., Asbury, S., Krozak, J.K., Wittkopf, K.: J2EE Antipatterns. Wiley, Hoboken (2003)
Rodriguez, J.M., Crasso, M., Zunino, A., Campo, M.: Automatically detecting opportunities for web service descriptions improvement. In: Conference on e-Business, e-Services and e-Society, pp. 139–150. Springer (2010)
Moha, N., Palma, F., Nayrolles, M., Conseil, B.J., Guéhéneuc, Y.-G., Baudry, B., Jézéquel, J.-M.: Specification and detection of SOA antipatterns. In: International Conference on Service-Oriented Computing, pp. 1–16. Springer (2012)
Ouni, A., Gaikovina Kula, R., Kessentini, M., Inoue, K.: Web service antipatterns detection using genetic programming. In: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, pp. 1351–1358. ACM (2015)
Palma, F., Moha, N., Tremblay, G., Guéhéneuc, Y.-G.: Specification and detection of SOA antipatterns in web services. In: European Conference on Software Architecture, pp. 58–73. Springer (2014)
Nayrolles, M., Palma, F., Moha, N., Guéhéneuc, Y.-G.: Soda: a tool support for the detection of SOA antipatterns. In: International Conference on Service-Oriented Computing, pp. 451–455. Springer (2012)
Marinescu, R.: Detection strategies: metrics-based rules for detecting design flaws. In: 20th IEEE International Conference on Software Maintenance, 2004. Proceedings, pp. 350–359. IEEE (2004)
Chidamber, S.R., Kemerer, C.F.: A metrics suite for object oriented design. IEEE Trans. Softw. Eng. 20(6), 476–493 (1994)
Vasilescu, B., Serebrenik, A., van den Brand, M.: You can (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Tummalapalli, S., Kumar, L., Bhanu Murthy, N.L. (2020). Detection of Web Service Anti-patterns Using Machine Learning Framework. In: Singh, J., Bilgaiyan, S., Mishra, B., Dehuri, S. (eds) A Journey Towards Bio-inspired Techniques in Software Engineering. Intelligent Systems Reference Library, vol 185. Springer, Cham. https://doi.org/10.1007/978-3-030-40928-9_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-40928-9_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-40927-2
Online ISBN: 978-3-030-40928-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)