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

被引:1
作者
Huizinga, Austin [1 ]
Parker, Garrett [1 ]
Abdelfattah, Amr S. [1 ]
Li, Xiaozhou [2 ,3 ]
Cerny, Tomas [4 ]
Taibi, Davide [2 ,3 ]
机构
[1] Baylor Univ, Waco, TX 76706 USA
[2] Tampere Univ, Tampere, Finland
[3] Oulu Univ, Oulu, Finland
[4] Univ Arizona, SIE, Tucson, AZ 85724 USA
来源
NEXT GENERATION DATA SCIENCE, SDSC 2023 | 2024年 / 2113卷
基金
美国国家科学基金会;
关键词
Microservices; Visualization; Smells detection; anti-patterns;
D O I
10.1007/978-3-031-61816-1_15
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:212 / 227
页数:16
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