Unsupervised Learning for Detecting Refactoring Opportunities in Service-Oriented Applications

详细信息    查看全文

• 关键词：Service ; oriented applications ; Web services ; Unsupervised machine learning ; Web service description language ; Service understandability ; Software visualization
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9828
• 期：1
• 页码：335-342
• 全文大小：851 KB
• 参考文献：1.Crasso, M., Zunino, A., Campo, M.: Awsc: an approach to web service classification based on machine learning techniques. Revista Iberoamericana de Inteligencia Artificial 12(37), 25–36 (2008)
  2.Dong, X., Halevy, A., Madhavan, J., Nemes, E., Zhang, J.: Similarity search for web services. In: 30th International Conference on Very large data bases, pp. 372–383. VLDB Endowment (2004)
  3.Elgazzar, K., Hassan, A.E., Martin, P.: Clustering wsdl documents to bootstrap the discovery of web services. In: IEEE International Conference on Web Services, pp. 147–154. IEEE (2010)
  4.Erickson, J., Siau, K.: Web services, service-oriented computing, and service-oriented architecture: Separating hype from reality. Principle Advancements in Database Management Technologies: New Applications and Frameworks, p. 176 (2009)
  5.Fisher, D.H.: Knowledge acquisition via incremental conceptual clustering. Mach. Learn. 2(2), 139–172 (1987)
  6.Fokaefs, M., Mikhaiel, R., Tsantalis, N., Stroulia, E., Lau, A.: An empirical study on web service evolution. In: IEEE International Conference on Web Services, pp. 49–56. IEEE (2011)
  7.Hop, W., de Ridder, S., Frasincar, F., Hogenboom, F.: Using hierarchical edge bundles to visualize complex ontologies in glow. In: Proceedings of the 27th Annual ACM Symposium on Applied Computing, pp. 304–311. ACM (2012)
  8.Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis, vol. 344. Wiley, Hoboken (2009)MATH
  9.Kuhn, A., Ducasse, S., Gírba, T.: Semantic clustering: identifying topics in source code. Inf. Softw. Technol. 49(3), 230–243 (2007)CrossRef
  10.Kumara, B.T., Yaguchi, Y., Paik, I., Chen, W.: Clustering and spherical visualization of web services. In: IEEE International Conference on Services Computation, pp. 89–96. IEEE (2013)
  11.Liu, W., Wong, W.: Web service clustering using text mining techniques. Int. J. Agent-Oriented Softw. Eng. 3(1), 6–26 (2009)CrossRef
  12.Ma, J., Zhang, Y., He, J.: Efficiently finding web services using a clustering semantic approach. In: International Workshop on Context Enabled Source and Service Selection, Integration and Adaptation, p. 5. ACM (2008)
  13.MacQueen, J., et al.: Some methods for classification and analysis of multivariate observations. In: 5th Berkeley Symposium on Mathematical Statistics and Probability, California, USA, vol. 1, pp. 281–297 (1967)
  14.Mateos, C., Crasso, M., Zunino, A., Coscia, J.L.O.: Detecting wsdl bad practices in code-first web services. Int. J. Web Grid Serv. 7(4), 357–387 (2011)CrossRef
  15.Nieweglowski, L.: clv: cluster validation techniques. R package version 0.3-2. http://​cran.​r-project.​org/​web/​packages/​clv
  16.Pelleg, D., Moore, A.W., et al.: X-means: extending k-means with efficient estimation of the number of clusters. In: ICML, pp. 727–734 (2000)
  17.Rodriguez, J.M., Crasso, M., Mateos, C., Zunino, A., Campo, M.: Bottom-up and top-down cobol system migration to web services. IEEE Internet Comput. 17(2), 44–51 (2013)
  18.Sabou, M., Pan, J.: Towards semantically enhanced web service repositories. Web Semant. Sci. Serv. Agents WWW 5(2), 142–150 (2007)CrossRef
  19.Teyseyre, A.R., Campo, M.R.: An overview of 3d software visualization. IEEE Trans. Vis. Comput. Graph. 15(1), 87–105 (2009)CrossRef
  20.Webster, D., Townend, P., Xu, J.: Interface refactoring in performance-constrained web services. In: 2012 IEEE 15th International Symposium on Object/Component/Service-Oriented Real-Time Distributed Computing (ISORC), pp. 111–118. IEEE (2012)
  
• 作者单位：Guillermo Rodríguez (15)
  Álvaro Soria (15)
  Alfredo Teyseyre (15)
  Luis Berdun (15)
  Marcelo Campo (15)
  
  15. ISISTAN Research Institute (CONICET-UNICEN), Campus Universitario, Paraje Arroyo Seco, B7001BBO, Tandil, Bs. As., Argentina
  
• 丛书名：Database and Expert Systems Applications
• ISBN：978-3-319-44406-2
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9828

文摘

Service-Oriented Computing (SOC) has been widely used for building distributed and enterprise-wide software applications. One major problem in this kind of applications is their growth; as size and complexity of applications increase, the probability of duplicity of code increases, among other refactoring issues. This paper proposes an unsupervised learning approach to assist software developers in detecting refactoring opportunities in service-oriented applications. The approach gathers non-refactored Web Service Description Language (WSDL) documents and applies clustering and visualization techniques to deliver a list of refactoring suggestions to start working on the refactoring process. We evaluated our approach using two real-life case-studies by using internal validity criteria for the clustering quality.