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A curated Dataset of Microservices-Based Systems

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arxiv 1909.03249 v1 pith:64QEETZK submitted 2019-09-07 cs.SE

classification cs.SE
keywords datasetprojectspatternsmicroservicemicroservicessystemsanalysisarchitectural
verification ladder T0 review T1 audit T2 compute T3 formal
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Microservices based architectures are based on a set of modular, independent and fault-tolerant services. In recent years, the software engineering community presented studies investigating potential, recurrent, effective architectural patterns in microservices-based architectures, as they are very essential to maintain and scale microservice-based systems. Indeed, the organizational structure of such systems should be reflected in so-called microservice architecture patterns, that best fit the projects and development teams needs. However, there is a lack of public repositories sharing open sources projects microservices patterns and practices, which could be beneficial for teaching purposes and future research investigations. This paper tries to fill this gap, by sharing a dataset, having a first curated list microservice-based projects. Specifically, the dataset is composed of 20 open-source projects, all using specific microservice architecture patterns. Moreover, the dataset also reports information about inter-service calls or dependencies of the aforementioned projects. For the analysis, we used two different tools (1) SLOCcount and (2) MicroDepGraph to get different parameters for the microservice dataset. Both the microservice dataset and analysis tool are publicly available online. We believe that this dataset will be highly used by the research community for understanding more about microservices architectural and dependencies patterns, enabling researchers to compare results on common projects.

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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. Extracting Overlapping Microservices from Monolithic Code via Deep Semantic Embeddings and Graph Neural Network-Based Soft Clustering

    cs.SE 2025-08 reject novelty 6.0 of 10

    Mo2oM assigns classes to overlapping microservices using UniXcoder embeddings and NOCD soft clustering, claiming large gains in modularity metrics over hard-clustering baselines on four monoliths.

  2. Leveraging Network Methods for Hub-like Microservice Detection

    cs.SE 2025-06 conditional novelty 5.0 of 10

    On 25 microservice dependency graphs, the Erdos-Renyi compression-based hub detector found hub-like services with the highest precision, while degree-based and centrality-based methods mostly disagreed with each other.

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