UniDoSA: The Unified Specification and Detection of Service Antipatterns
2019 (English)In: IEEE Transactions on Software Engineering, ISSN 0098-5589, E-ISSN 1939-3520, Vol. 45, no 10, p. 1024-1053Article in journal (Refereed) Published
Abstract [en]
Service-based Systems (SBSs) are developed on top of diverse Service-Oriented Architecture (SOA) technologies or architectural styles. Like any other complex systems, SBSs face both functional and non-functional changes at the design or implementation-level. Such changes may degrade the design quality and quality of service (QoS) of the services in SBSs by introducing poor solutions-service antipatterns. The presence of service antipatterns in SBSs may hinder the future maintenance and evolution of SBSs. Assessing the quality of design and QoS of SBSs through the detection of service antipatterns may ease their maintenance and evolution. However, the current literature lacks a unified approach for modelling and evaluating the design of SBSs in term of design quality and QoS. To address this lack, this paper presents a meta-model unifying the three main service technologies: REST, SCA, and SOAP. Using the meta-model, it describes a unified approach, UniDoSA (Unified Specification and Detection of Service Antipatterns), supported by a framework, SOFA (Service Oriented Framework for Antipatterns), for modelling and evaluating the design quality and QoS of SBSs. We apply and validate UniDoSA on: (1) 18 RESTful APIs, (2) two SCA systems with more than 150 services, and (3) more than 120 SOAP Web services. With a high precision and recall, the detection results provide evidence of the presence of service antipatterns in SBSs, which calls for future studies of their impact on QoS.
Place, publisher, year, edition, pages
IEEE, 2019. Vol. 45, no 10, p. 1024-1053
Keywords [en]
Antipatterns, service-based systems, REST, SCA, SOAP, web services, specification, detection, quality of service, design, software maintenance and evolution
National Category
Software Engineering
Research subject
Computer Science, Software Technology
Identifiers
URN: urn:nbn:se:lnu:diva-90838DOI: 10.1109/TSE.2018.2819180ISI: 000502113300005Scopus ID: 2-s2.0-85044354578OAI: oai:DiVA.org:lnu-90838DiVA, id: diva2:1384763
2020-01-102020-01-102022-04-12Bibliographically approved