Web Service Antipatterns Detection Using Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Ouni:2015:GECCO,
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author = "Ali Ouni and Raula {Gaikovina Kula} and
Marouane Kessentini and Katsuro Inoue",
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title = "Web Service Antipatterns Detection Using Genetic
Programming",
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booktitle = "GECCO '15: Proceedings of the 2015 Annual Conference
on Genetic and Evolutionary Computation",
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year = "2015",
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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",
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isbn13 = "978-1-4503-3472-3",
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pages = "1351--1358",
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keywords = "genetic algorithms, genetic programming, SBSE,
Search-Based Software Engineering",
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month = "11-15 " # jul,
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organisation = "SIGEVO",
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address = "Madrid, Spain",
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URL = "http://doi.acm.org/10.1145/2739480.2754724",
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DOI = "doi:10.1145/2739480.2754724",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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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.",
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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