A Hierarchical-DBSCAN Method for Extracting Microservices from Monolithic Applications

TL;DR

This paper introduces a hierarchical-DBSCAN based method to automatically identify microservices from monolithic applications by analyzing code similarities and dependencies, aiming to facilitate automated migration to microservice architecture.

Contribution

The paper presents a novel hierarchical clustering approach that leverages code interactions and domain terminology to extract microservices, outperforming existing methods in cohesion and interaction minimization.

Findings

Achieves up to 0.9 precision compared to human-designed microservices.

Generates more cohesive microservices with fewer inter-service interactions.

Outperforms five baseline methods in empirical evaluations.

Abstract

The microservices architectural style offers many advantages such as scalability, reusability and ease of maintainability. As such microservices has become a common architectural choice when developing new applications. Hence, to benefit from these advantages, monolithic applications need to be redesigned in order to migrate to a microservice based architecture. Due to the inherent complexity and high costs related to this process, it is crucial to automate this task. In this paper, we propose a method that can identify potential microservices from a given monolithic application. Our method takes as input the source code of the source application in order to measure the similarities and dependencies between all of the classes in the system using their interactions and the domain terminology employed within the code. These similarity values are then used with a variant of a density-based clustering algorithm to generate a hierarchical structure of the recommended microservices while identifying potential outlier classes. We provide an empirical evaluation of our approach through different experimental settings including a comparison with existing human-designed microservices and a comparison with 5 baselines. The results show that our method succeeds in generating microservices that are overall more cohesive and that have fewer interactions in-between them with up to 0.9 of precision score when compared to human-designed microservices.