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Detecting Microservice Anti-patterns Using Interactive Service Call Graphs: Effort Assessment
Conference proceeding   Peer reviewed

Detecting Microservice Anti-patterns Using Interactive Service Call Graphs: Effort Assessment

A Huizinga, G Parker, AS Abdelfattah, Xiaozhou Li, T Cerny and D Taibi
Next Generation Data Science: Second Southwest Data Science Conference, SDSC 2023, Waco, TX, USA, March 24–25, 2023, Revised Selected Papers , Vol.2113, pp.212-227
Communications in Computer and Information Science, 2113
Southwest Data Science Conference (Waco, 22/03/2024–22/03/2024)
2024
Handle:
https://hdl.handle.net/10863/52229

Abstract

Anti-patterns Microservices Smells detection Visualization
Together with the increasing adoption of microservices, detecting microservice anti-patterns has become a crucial practice. However, the number of tools supporting effective anti-pattern detection is limited . Though involving the human in the loop is useful, it is time-consuming and lacks the accuracy necessary to complete such a task. For such a purpose, we consider visualizing the microservice system architecture using the service view, specifically the service call graph. In this paper, we present a framework to visualize service call graphs in an interactive 3D node-edge model. Utilizing an intermediate representation of the microservice system architecture, we create our interactive model to allow for quicker, more accurate detection.
url
https://doi.org/10.1007/978-3-031-61816-1_15View

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