Comparison of Static Analysis Architecture Recovery Tools for Microservice Applications

TL;DR

This study compares static architecture recovery tools for microservice applications, highlighting their accuracy and exploring combinations to improve architecture detection, with the best tool achieving an F1-score of 0.86.

Contribution

It provides a comprehensive comparison of static architecture recovery tools for microservices and demonstrates how combining tools can enhance accuracy.

Findings

Best tool achieved an F1-score of 0.86.

Combining four tools increased F1-score to 0.91.

Static tools are suitable for CI/CD pipelines in microservice environments.

Abstract

Architecture recovery tools help software engineers obtain an overview of the structure of their software systems during all phases of the software development life cycle. This is especially important for microservice applications because they consist of multiple interacting microservices, which makes it more challenging to oversee the architecture. Various tools and techniques for architecture recovery (also called architecture reconstruction) have been presented in academic and gray literature sources, but no overview and comparison of their accuracy exists. This paper presents the results of a multivocal literature review with the goal of identifying architecture recovery tools for microservice applications and a comparison of the identified tools' architectural recovery accuracy. We focused on static tools since they can be integrated into fast-paced CI/CD pipelines. 13 such tools were identified from the literature and nine of them could be executed and compared on their capability of detecting different system characteristics. The best-performing tool exhibited an overall F1-score of 0.86. Additionally, the possibility of combining multiple tools to increase the recovery correctness was investigated, yielding a combination of four individual tools that achieves an F1-score of 0.91. Registered report: The methodology of this study has been peer-reviewed and accepted as a registered report at MSR'24: arXiv:2403.06941