Web Service Antipatterns Detection Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8010

@InProceedings{Ouni:2015:GECCO,

• author = "Ali Ouni and Raula {Gaikovina Kula} and Marouane Kessentini and Katsuro Inoue",
• title = "Web Service Antipatterns Detection Using Genetic Programming",
• booktitle = "GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation",
• year = "2015",
• editor = "Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr",
• isbn13 = "978-1-4503-3472-3",
• pages = "1351--1358",
• keywords = "genetic algorithms, genetic programming, SBSE, Search-Based Software Engineering",
• month = "11-15 " # jul,
• organisation = "SIGEVO",
• address = "Madrid, Spain",
• URL = "http://doi.acm.org/10.1145/2739480.2754724",
• DOI = "doi:10.1145/2739480.2754724",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• abstract = "Service-Oriented Architecture (SOA) is an emerging paradigm that has radically changed the way software applications are architected, designed and implemented. SOA allows developers to structure their systems as a set of ready-made, reusable and compostable services. The leading technology used today for implementing SOA is Web Services. Indeed, like all software, Web services are prone to change constantly to add new user requirements or to adapt to environment changes. Poorly planned changes may risk introducing antipatterns into the system. Consequently, this may ultimately leads to a degradation of software quality, evident by poor quality of service (QoS). In this paper, we introduce an automated approach to detect Web service antipatterns using genetic programming. Our approach consists of using knowledge from real-world examples of Web service antipatterns to generate detection rules based on combinations of metrics and threshold values. We evaluate our approach on a benchmark of 310 Web services and a variety of five types of Web service antipatterns. The statistical analysis of the obtained results provides evidence that our approach is efficient to detect most of the existing antipatterns with a score of 85percent of precision and 87percent of recall.",
• notes = "Also known as \cite{2754724} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)",

}

Genetic Programming entries for Ali Ouni Raula Gaikovina Kula Marouane Kessentini Katsuro Inoue

Citations