BOAM: A Business-oriented Identification Approach of Microservices within Legacy Systems. Mahmoudi, B., Trabelsi, I., Tamzalit, D., Moha, N., & Gu�h�neuc, Y. In Gaaloul, W., Sheng, M., & Yu, Q., editors, Proceedings of the 22<sup>nd</sup> International Conference on Service-Oriented Computing (ICSOC), pages 123–137, December, 2024. IEEE CS Press. 15 pages.![BOAM: A Business-oriented Identification Approach of Microservices within Legacy Systems [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
Paper abstract bibtex The microservices architecture (MSA) is highly popular for its scalability, deployability in the Cloud and compatibility with DevOps practices. Many companies are migrating their legacy systems to an MSA. They need to rely on automatic approaches to ease their migration while taking into account their business features. Existing migration approaches to an MSA often focus on technical features but neglect functional ones, which are essential for appropriate MS granularity. To address this lack, we introduce BOAM (Business Oriented identification Approach of Microservices), a hybrid approach that focuses on business decomposition by leveraging not only technical features, such as source code, but also business oriented artifacts, especially use cases. BOAM thus leverages static and semantic analyses of source code using nanoentities (data, operations or artifacts), followed by a semantic analysis of use cases to capture business features. For that, BOAM leans on machine learning, particularly clustering methods, to identify microservices through both technical (source code) and business (use cases) artifacts. The objective is to ensure that identified microservices are technically sound and meet specific business features of the company. Our evaluation shows that BOAM outperforms other state-of-the-art approaches to identify microservices, achieving an average precision of 74.51% and recall of 77.93%.
@INPROCEEDINGS{Mahmoudi24-ICSOC-BOAM,
AUTHOR = {Brahim Mahmoudi and Imen Trabelsi and Dalila Tamzalit and
Naouel Moha and Yann-Ga�l Gu�h�neuc},
BOOKTITLE = {Proceedings of the 22<sup>nd</sup> International Conference on Service-Oriented Computing (ICSOC)},
TITLE = {BOAM: A Business-oriented Identification Approach of
Microservices within Legacy Systems},
YEAR = {2024},
OPTADDRESS = {},
OPTCROSSREF = {},
EDITOR = {Walid Gaaloul and Michael Sheng and Qi Yu},
MONTH = {December},
NOTE = {15 pages.},
OPTNUMBER = {},
OPTORGANIZATION = {},
PAGES = {123–137},
PUBLISHER = {IEEE CS Press},
OPTSERIES = {},
OPTVOLUME = {},
KEYWORDS = {Topic: <b>Evolution patterns</b>,
Rubrique : <b>patrons d'�volution</b>, Conference: ICSOC},
URL = {http://www.ptidej.net/publications/documents/ICSOC24.doc.pdf},
PDF = {http://www.ptidej.net/publications/documents/ICSOC24.ppt.pdf},
ABSTRACT = {The microservices architecture (MSA) is highly popular
for its scalability, deployability in the Cloud and compatibility
with DevOps practices. Many companies are migrating their legacy
systems to an MSA. They need to rely on automatic approaches to ease
their migration while taking into account their business features.
Existing migration approaches to an MSA often focus on technical
features but neglect functional ones, which are essential for
appropriate MS granularity. To address this lack, we introduce BOAM
(Business Oriented identification Approach of Microservices), a
hybrid approach that focuses on business decomposition by leveraging
not only technical features, such as source code, but also business
oriented artifacts, especially use cases. BOAM thus leverages static
and semantic analyses of source code using nanoentities (data,
operations or artifacts), followed by a semantic analysis of use
cases to capture business features. For that, BOAM leans on machine
learning, particularly clustering methods, to identify microservices
through both technical (source code) and business (use cases)
artifacts. The objective is to ensure that identified microservices
are technically sound and meet specific business features of the
company. Our evaluation shows that BOAM outperforms other
state-of-the-art approaches to identify microservices, achieving an
average precision of 74.51% and recall of 77.93%.}
}
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