  Microservice architecture has become a dominant architectural style in the
service-oriented software industry. Poor practices in the design and
development of microservices are called microservice bad smells. In
microservice bad smells research, the detection of these bad smells relies on
feature data from microservices. However, there is a lack of an appropriate
open-source microservice feature dataset. The availability of such datasets may
contribute to the detection of microservice bad smells unexpectedly. To address
this research gap, this paper collects a number of open-source microservice
systems utilizing Spring Cloud. Additionally, feature metrics are established
based on the architecture and interactions of Spring Boot style microservices.
And an extraction program is developed. The program is then applied to the
collected open-source microservice systems, extracting the necessary
information, and undergoing manual verification to create an open-source
feature dataset specific to microservice systems using Spring Cloud. The
dataset is made available through a CSV file. We believe that both the
extraction program and the dataset have the potential to contribute to the
study of micro-service bad smells.
