On the maintenance support for microservice-based systems through the specification and the detection of microservice antipatterns
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 ant...
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| Published in | The Journal of systems and software Vol. 204; p. 111755 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
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Elsevier Inc
01.10.2023
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| Subjects | |
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| 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% and a recall of 89%. Thus, our approach can help developers assert and improve the quality of their microservices and development practices.
•Microservice antipatterns may hinder the maintenance of microservice-based systems.•We propose MARS, a highly automated tool to detect 16 microservice antipatterns.•We validate the detection results of MARS on 24 microservice-based systems.•We accurately detect 16 antipatterns with a precision of 82% and a recall of 89%. |
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| AbstractList | 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% and a recall of 89%. Thus, our approach can help developers assert and improve the quality of their microservices and development practices.
•Microservice antipatterns may hinder the maintenance of microservice-based systems.•We propose MARS, a highly automated tool to detect 16 microservice antipatterns.•We validate the detection results of MARS on 24 microservice-based systems.•We accurately detect 16 antipatterns with a precision of 82% and a recall of 89%. |
| ArticleNumber | 111755 |
| Author | Tighilt, Rafik Trabelsi, Imen Guéhéneuc, Yann-Gaël Madern, Loïc Moha, Naouel Abdellatif, Manel |
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| Cites_doi | 10.1109/ICSA.2018.00014 10.1109/MS.2018.2141031 10.1007/978-3-030-49418-6_21 10.3390/app10217800 10.1109/TSE.2009.50 10.1145/3387906.3388625 10.1109/ICSAW.2017.48 10.1126/science.272.5265.1131 10.1016/j.jss.2021.111061 10.1145/3424771.3424812 |
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