The μTOSCA toolchain: Mining, analyzing, and refactoring microservice‐based architectures

Abstract

Summary Exploiting microservices to architect enterprise applications is becoming commonplace. This makes it crucial to provide some support for designing and analyzing microservice‐based applications, for example, for understanding whether a microservice‐based application adheres to the main design principles of microservices and for choosing how to refactor it when this is not the case. To provide such support, in this article we present the μTOSCA toolchain. More precisely, we first introduce the μTOSCA model to represent the architecture of microservice‐based applications with the OASIS standard TOSCA. We then describe a technique to automatically mine the architecture of a microservice‐based application and represent it with μTOSCA, given the Kubernetes deployment of the application. We also present a methodology to analyze the μTOSCA representation of a microservice‐based architecture to systematically identify the architectural smells potentially affecting the corresponding application and to resolve them. Finally, we present two prototype tools, μMiner and μFreshener, implementing our mining solution and the support for identifying and resolving architectural smells in microservice‐based applications, respectively. We then assess —by discussing some case studies— how effectively μMiner, μFreshener, and the μTOSCA toolchain can support researchers and practitioners working with microservices.

Static analysis tools in the era of cloud-native systems

“…However, we must also assume one important thing; the static analysis does not only consume code or code changes. The cloud-native design typically involves build files and container configuration files in the repository, and these files can be easily analyzed to help determine topology [9,19,11] and involved technology.…”

On Microservice Analysis and Architecture Evolution: A Systematic Mapping Study

“…They employed static and dynamic analysis to achieve the static structure in addition to runtime behavior characteristics of a monolithic application. Soldani et al [22] combined static and dynamic mining of information of a microservice system to obtain a methodology that identifies architectural smells. The authors used Kubernetes deployment files instead of source code for static mining.…”

“…JCallGraph is a newly developed tool by Liu et al [53] for tracing and monitoring using the graph-based approach. µTOSCA is proposed and developed by Saldana et al [22] to analyze and detect architectural smells. Klinaku et al [60] used the existing tool Palladio [68] to conduct a case study to analyze the performance of microservice applications.…”

“…Works identifying technical debt are also closely related to the concept of antipatterns, and the surveys performed by Bogner et al [65] and Toledo et al [63] and the solutions they proposed show promise for preventing technical debt from crippling microservice evolution. Soldani et al developed a tool, µTOSCA, to identify and resolve architectural smells [22]. µTOSCA uses the Kubernetes deployment file to resolve architectural smells using the developed tool µFRESHENER via refactoring.…”

Survey on Tools and Techniques Detecting Microservice API Patterns

“…This often leads to an alternative directiondynamic analysis. Dynamic analysis tools can operate on the decentralized perspective [8]. However, the dynamic analysis does not reveal a comprehensive detail of the system (e.g., concerning the actual implementation of the service).…”