On the Maintenance Support for Microservice-based Systems through the Specification and the Detection of Microservice Antipatterns. Tighilt, R., Abdellatif, M., Trabelsi, I., Madern, L., Moha, N., & Gu�h�neuc, Y. Journal of Systems and Software (JSS), 204:111755, Elsevier, October, 2023. 16 pages.
Paper abstract bibtex The software industry is currently moving from monolithic to microservice architectures, which are made up of independent, reusable, and fine-grained services. A lack of understanding of the core concepts of microservice architectures can lead to poorly designed systems that include microservice antipatterns. These microservice antipatterns may affect the quality of services and hinder the maintenance and evolution of software systems. The specification and detection of microservice antipatterns could help in evaluating and assessing the design quality of systems. Several research works have studied patterns and antipatterns in microservice-based systems, but the automatic detection of these antipatterns is still in its infancy. We propose MARS (Microservice Antipatterns Research Software), a fully automated approach supported by a framework for specifying and identifying microservice antipatterns. Using MARS, we specify and identify 16 microservice antipatterns in 24 microservice-based systems. The results show that MARS can effectively detect microservice antipatterns with an average precision of 82\NOand a recall of 89%. Thus, our approach can help developers assert and improve the quality of their microservices and development practices.
@ARTICLE{Tighilt23-JSS-MicroserviceAntipatterns,
AUTHOR = {Rafik Tighilt and Manel Abdellatif and Imen Trabelsi and
Lo�c Madern and Naouel Moha and Yann-Ga�l Gu�h�neuc},
JOURNAL = {Journal of Systems and Software (JSS)},
TITLE = {On the Maintenance Support for Microservice-based
Systems through the Specification and the Detection of Microservice
Antipatterns},
YEAR = {2023},
MONTH = {October},
NOTE = {16 pages.},
OPTNUMBER = {},
PAGES = {111755},
VOLUME = {204},
EDITOR = {Paris Avgeriou and David Shepherd},
KEYWORDS = {Topic: <b>Evolution patterns</b>, Venue: <b>JSS</b>},
PUBLISHER = {Elsevier},
URL = {http://www.ptidej.net/publications/documents/JSS23.doc.pdf},
ABSTRACT = {The software industry is currently moving from
monolithic to microservice architectures, which are made up of
independent, reusable, and fine-grained services. A lack of
understanding of the core concepts of microservice architectures can
lead to poorly designed systems that include microservice
antipatterns. These microservice antipatterns may affect the quality
of services and hinder the maintenance and evolution of software
systems. The specification and detection of microservice antipatterns
could help in evaluating and assessing the design quality of systems.
Several research works have studied patterns and antipatterns in
microservice-based systems, but the automatic detection of these
antipatterns is still in its infancy. We propose MARS (Microservice
Antipatterns Research Software), a fully automated approach supported
by a framework for specifying and identifying microservice
antipatterns. Using MARS, we specify and identify 16 microservice
antipatterns in 24 microservice-based systems. The results show that
MARS can effectively detect microservice antipatterns with an average
precision of 82\NOand a recall of 89\%. Thus, our approach can help
developers assert and improve the quality of their microservices and
development practices.}
}
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A lack of understanding of the core concepts of microservice architectures can lead to poorly designed systems that include microservice antipatterns. These microservice antipatterns may affect the quality of services and hinder the maintenance and evolution of software systems. The specification and detection of microservice antipatterns could help in evaluating and assessing the design quality of systems. Several research works have studied patterns and antipatterns in microservice-based systems, but the automatic detection of these antipatterns is still in its infancy. We propose MARS (Microservice Antipatterns Research Software), a fully automated approach supported by a framework for specifying and identifying microservice antipatterns. Using MARS, we specify and identify 16 microservice antipatterns in 24 microservice-based systems. The results show that MARS can effectively detect microservice antipatterns with an average precision of 82\\NOand a recall of 89%. 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