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Comparison of Static Analysis Architecture Recovery Tools for Microservice Applications

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arxiv 2403.06941 v1 pith:JVMUV4EB submitted 2024-03-11 cs.SE

Simon Schneider , Alexander Bakhtin , Xiaozhou Li , Jacopo Soldani , Antonio Brogi , Tomas Cerny , Riccardo Scandariato , Davide Taibi This is my paper · ORCID
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keywords toolsarchitecturerecoveryapplicationsmicroserviceoverviewsoftwarestatic
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Architecture recovery tools help software engineers obtain an overview of their software systems during all phases of the software development lifecycle. This is especially important for microservice applications because their distributed nature makes it more challenging to oversee the architecture. Various tools and techniques for this task are presented in academic and grey literature sources. Practitioners and researchers can benefit from a comprehensive overview of these tools and their abilities. However, no such overview exists that is based on executing the identified tools and assessing their outputs regarding effectiveness. With the study described in this paper, we plan to first identify static analysis architecture recovery tools for microservice applications via a multi-vocal literature review, and then execute them on a common dataset and compare the measured effectiveness in architecture recovery. We will focus on static approaches because they are also suitable for integration into fast-paced CI/CD pipelines.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Comparison of Static Analysis Architecture Recovery Tools for Microservice Applications

    cs.SE 2024-12 conditional novelty 6.0 of 10

    Across 17 microservice applications, Code2DFD achieved the highest individual F1-score of 0.86 among nine static architecture recovery tools, with an in-sample four-tool combination reaching 0.91.

  2. Network Centrality as a New Perspective on Microservice Architecture

    cs.SE 2025-01 conditional novelty 5.0 of 10

    Centrality metrics calculated on service dependency graphs are only weakly to moderately correlated with traditional size, complexity, and quality metrics across 53 microservices, suggesting they capture a distinct ar...

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