research-article

Web Service Antipatterns Detection Using Genetic Programming

Authors:

Raula Gaikovina Kula,

Marouane Kessentini,

Katsuro InoueAuthors Info & Claims

GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

Pages 1351 - 1358

https://doi.org/10.1145/2739480.2754724

Published: 11 July 2015 Publication History

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 85% of precision and 87% of recall.

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Cited By

Tummalapalli SKumar LNeti LKrishna A(2023)A Comparative Analysis on the Detection of Web Service Anti-Patterns Using Various MetricsProceedings of the 16th Innovations in Software Engineering Conference10.1145/3578527.3578534(1-7)Online publication date: 23-Feb-2023
https://dl.acm.org/doi/10.1145/3578527.3578534
Daaji MOuni AGammoudi MBouktif SMkaouer M(2023)BPEL process defects prediction using multi-objective evolutionary searchJournal of Systems and Software10.1016/j.jss.2023.111767204:COnline publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1016/j.jss.2023.111767
Cortellessa VDi Pompeo DStoico VTucci M(2023)Many-objective optimization of non-functional attributes based on refactoring of software modelsInformation and Software Technology10.1016/j.infsof.2023.107159157:COnline publication date: 1-May-2023
https://dl.acm.org/doi/10.1016/j.infsof.2023.107159
Show More Cited By

Index Terms

Web Service Antipatterns Detection Using Genetic Programming
1. Computing methodologies
  1. Artificial intelligence
    1. Control methods
    2. Search methodologies
2. Theory of computation
  1. Design and analysis of algorithms
    1. Algorithm design techniques
      1. Dynamic programming

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cover image ACM Conferences

GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

July 2015

1496 pages

ISBN:9781450334723

DOI:10.1145/2739480

Editor:
Sara Silva
Universidade de Lisboa, Portugal
,
General Chair:
Anna I. Esparcia-Alcázar
Universitat Politècnica de València, Spain

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 July 2015

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Research-article

Funding Sources

Japan Society for the Promotion of Science Grant-in-Aid for Scientific Research
Osaka University Program for Promoting International Joint Research

Conference

GECCO '15

Sponsor:

SIGEVO

GECCO '15: Genetic and Evolutionary Computation Conference

July 11 - 15, 2015

Madrid, Spain

Acceptance Rates

GECCO '15 Paper Acceptance Rate 182 of 505 submissions, 36%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Tummalapalli SKumar LNeti LKrishna A(2023)A Comparative Analysis on the Detection of Web Service Anti-Patterns Using Various MetricsProceedings of the 16th Innovations in Software Engineering Conference10.1145/3578527.3578534(1-7)Online publication date: 23-Feb-2023
https://dl.acm.org/doi/10.1145/3578527.3578534
Daaji MOuni AGammoudi MBouktif SMkaouer M(2023)BPEL process defects prediction using multi-objective evolutionary searchJournal of Systems and Software10.1016/j.jss.2023.111767204:COnline publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1016/j.jss.2023.111767
Cortellessa VDi Pompeo DStoico VTucci M(2023)Many-objective optimization of non-functional attributes based on refactoring of software modelsInformation and Software Technology10.1016/j.infsof.2023.107159157:COnline publication date: 1-May-2023
https://dl.acm.org/doi/10.1016/j.infsof.2023.107159
Kumar LTummalapalli SRathi SMurthy LKrishna AMisra S(2023)Machine learning with word embedding for detecting web-services anti-patternsJournal of Computer Languages10.1016/j.cola.2023.10120775(101207)Online publication date: Jun-2023
https://doi.org/10.1016/j.cola.2023.101207
Gupta RSingh S(2023)A Novel Transfer Learning Method for Code Smell Detection on Heterogeneous Data: A Feasibility StudySN Computer Science10.1007/s42979-023-02157-64:6Online publication date: 28-Sep-2023
https://doi.org/10.1007/s42979-023-02157-6
Tummalapalli S(2022)Role of WSDL Metrics in the Detection of Web Service Anti-PatternsProceedings of the 15th Innovations in Software Engineering Conference10.1145/3511430.3511459(1-4)Online publication date: 24-Feb-2022
https://dl.acm.org/doi/10.1145/3511430.3511459
Abid CGaaloul KKessentini MAlizadeh V(2022)What Refactoring Topics Do Developers Discuss? A Large Scale Empirical Study Using Stack OverflowIEEE Access10.1109/ACCESS.2021.314003610(56362-56374)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2021.3140036
Tummalapalli Skumar LLalita Bhanu Murthy N(2022)Web Service Anti-patterns Prediction Using LSTM with Varying Embedding SizesAdvanced Information Networking and Applications10.1007/978-3-030-99584-3_35(399-410)Online publication date: 31-Mar-2022
https://doi.org/10.1007/978-3-030-99584-3_35
Almarimi NOuni AChouchen MMkaouer M(2022)Improving the detection of community smells through socio‐technical and sentiment analysisJournal of Software: Evolution and Process10.1002/smr.250535:6Online publication date: 2-Sep-2022
https://dl.acm.org/doi/10.1002/smr.2505
Almarimi NOuni AChouchen MMkaouer MSpinellis DGousios GChechik MDi Penta M(2021)csDetector: an open source tool for community smells detectionProceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3468264.3473121(1560-1564)Online publication date: 20-Aug-2021
https://dl.acm.org/doi/10.1145/3468264.3473121
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