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From static code analysis to visual models of microservice architecture.

Authors :
Cerny, Tomas
Abdelfattah, Amr S.
Yero, Jorge
Taibi, Davide
Source :
Cluster Computing. Jul2024, Vol. 27 Issue 4, p4145-4170. 26p.
Publication Year :
2024

Abstract

Microservice architecture is the mainstream driver for cloud-native systems. It brings various benefits to the development process, such as enabling decentralized development and evolution of self-contained system parts, facilitating their selective scalability. However, new challenges emerge in such systems as the system-holistic quality assurance becomes difficult. It becomes hard to maintain the desired system architecture since many teams are involved in the development process and have greater autonomy. Without instruments and practices to coordinate teams and assess the system as a whole, the system is prone to architectural degradation. To face such challenges, various architectural aspects of the system should be accessible to practitioners. It would give them a better understanding of interconnections and dependencies among the microservice they manage and the context of the entire system. This manuscript provides the perspective on uncovering selected system architectural views using static code analysis. It demonstrates that holistic architectural views can be effectively derived from the system codebase(s), highlighting dependencies across microservices. Such new perspectives will aid practitioners in making informed decisions when intending to change and evolve the system. Moreover, with such a new instrument for system holistic assessment, we quickly realize that human experts must cope with another problem, the evergrowing scales of cloud-native systems. To elaborate on the topic, this manuscript examines how static analysis outcomes can be transformed into interactive architectural visualizations to assist practitioners in handling large-scale complexities. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
13867857
Volume :
27
Issue :
4
Database :
Academic Search Index
Journal :
Cluster Computing
Publication Type :
Academic Journal
Accession number :
178805446
Full Text :
https://doi.org/10.1007/s10586-024-04394-7