Applying Text Classification Algorithms in Web Services Robustness Testing



Testing web services for robustness is an effective way of disclosing software bugs. However, when executing robustness tests, a very large amount of service responses has to be manually classified to distinguish regular responses from responses that indicate robustness problems. Besides requiring a large amount of time and effort, this complex classification process can easily lead to errors resulting from the human intervention in such a laborious task. Text classification algorithms have been applied successfully in many contexts (e.g., spam identification, text categorization, etc) and are considered a powerful tool for the successful automation of several classification-based tasks. In this paper we present a study on the applicability of five widely used text classification algorithms in the context of web services robustness testing. In practice, we assess the effectiveness of Support Vector Ma-chines, Naïve Bayes, Large Linear Classification, K-nearest neighbor (Ibk), and Hyperpipes in classifying web services responses. Results indicate that these algorithms can be effec-tively used to automate the identification of robustness issues while reducing human intervention. However, in all mechanisms there are cases of misclassified responses, which means that there is space for improvement.


web services, robustness classification


Web services robustness classification


29th IEEE International Symposium on Reliable Distributed Systems (SRDS 2010), October 2010

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Year 2013 : 2 citations

 Ali Shahrokni, Robert Feldt, "A systematic review of software robustness", Information and Software Technology, Volume 55, Issue 1, January 2013.

 D Di Leo, D Cotroneo, F Garofalo "Robustness Evaluation of Software Systems through Fault Injection", Phd Thesis, `
Universita degli Studi di Napoli Federico II, 2013

Year 2012 : 3 citations

 1. Guo Hongjian and Chen Yifei, “Web Classification Algorithm Using Support Vector Machine and Particle Swarm Optimization,” International Journal of Advancements in Computing Technology (IJACT) 4, no. 17.60, doi:10.4156/ijact, September 2012.

 2. Gizelle Sandrini Lemos and Eliane Martins, “Specification-guided Golden Run for Analysis of Robustness Testing Results”, 2012 IEEE Sixth International Conference on Software Security and Reliability (SERE), June 2012.

 4. Mustafa Bozkurt, Mark Harman, Youssef Hassoun, "Testing & Verification in Service-Oriented Architecture: A Survey", Software Testing, Verification and Reliability (STVR), Issn: 1099-1689, Wiley InterScience, 2012.

Year 2011 : 3 citations

 1. Dareen Abdelmoneim, “Semantic Deontic Modeling and Text Classification for Supporting Automated Environmental Compliance Checking in Construction”, MSc Thesis (Civil Engineering), University of Illinois at Urbana-Champaign, Illinois, USA, November 2011.

 2. Dareen Salama and Nora El-Gohary, “Natural Language Processing for Automated Regulatory and Contractual Document Analysis,” Annual Conference of the Canadian Society for Civil Engineering 2011, 2897–2906. Vol. 4, Ottawa, Ontario, Canada, ISBN: 978-1-61839-218-3, 14-17 June 2011.

 3. Ying Ma, Fei Wang, Jian Wu, “Locality Sensitive Hashing Based Service Classification”, The 5th International Conference on Management and Service Science (MASS 2011), Wuhan, China, August 12-14, 2011.