Weighing the Evidence: On Relationship Types in Microservice Extraction
The microservice-based architecture – a SOA-inspired principle of dividing systems into components that communicate with each other using language-agnostic APIs – has gained increased popularity in industry. Yet, migrating a monolithic application to microservices is a challenging task. A number of automated microservice extraction techniques have been proposed to help developers with the migration complexity. These techniques, at large, construct a graph-based representation of an application and cluster its elements into service candidates. The techniques vary by their decomposition goals and, subsequently, types of relationships between application elements that they consider – structural, semantic term similarity, and evolutionary – with each technique utilizing a fixed subset and weighting of these relationship types.
In this paper, we perform a multi-method exploratory study with 10 industrial practitioners to investigate (1) the applicability and usefulness of different relationships types during the microservice extraction process and (2) expectations practitioners have for tools utilizing such relationships. Our results show that practitioners often need a ``what-if'' analysis tool that simultaneously considers multiple relationship types during the extraction process and that there is no fixed way to weight these relationships. Our study also identifies organization- and application-specific considerations that lead practitioners to prefer certain relationship types over others, e.g., the age of the codebase and languages spoken in the organization. It outlines possible strategies to help developers during the extraction process, e.g., the ability to iteratively filter and customize relationships.
Wed 19 MayDisplayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
02:00 - 02:30
|Weighing the Evidence: On Relationship Types in Microservice Extraction
Lisa Kirby University of British Columbia, Canada, Evelien Boerstra , Zachary John Christopher Anderson , Julia Rubin University of British Columbia, CanadaPre-print Media Attached
|Comprehensive Integration of API Usage Patterns
ResearchPre-print Media Attached
|Analyzing bug fix for automatic bug cause classification (JSS)
Zhen Ni , Bin Li Yangzhou University, Xiaobing Sun Yangzhou University, Tianhao Chen , Ben Tang , Xinchen ShiMedia Attached