Comparing the Similarity of OpenAPI-Based Microservices
Zhongyi Lu, Declan T. Delaney and David Lillis
In Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing (SAC 2024), Avila, Spaind, 2024.
Abstract
Microservices constitute the state of the art for implementing distributed systems and have been seen as a potential solution towards open systems. The characteristics of open systems require struc- tured microservice management, including grouping microservices that are functionally similar. Microservices use RESTful APIs, often documented via OpenAPI specifications, to demonstrate their functionalities. Existing similarity metrics for microservice APIs have primarily focused on individual RESTful endpoints. However, understanding the full functionality of a microservice within an open system requires that the entirety of its OpenAPI documentation be considered. Thus, an approach that can compute a measure of similarity between entire microservice definitions in open environments is needed. In this paper, we propose an approach that can extract key information from the OpenAPI descriptions of microservices using Natural Language Processing (NLP) techniques, vectorise the extracted information using GLoVe embeddings, and cluster similar microservices using embedded API file vectors. Evaluations were conducted on real-world OpenAPI documents to demonstrate the effectiveness of the proposed approach.