Web Service Antipatterns Detection Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  author =       "Ali Ouni and Raula {Gaikovina Kula} and 
                 Marouane Kessentini and Katsuro Inoue",
  title =        "Web Service Antipatterns Detection Using Genetic
  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
  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