A Model-Driven Approach Towards Automatic Migration to Microservices

Bucchiarone, Antonio; Soysal, Kemal; Guidi, Claudio

doi:10.1007/978-3-030-39306-9_2

Cited by 13 publications

(6 citation statements)

References 22 publications

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“…Semantic similarity-based extraction methods use algorithms trained to detect linguistic patterns in order to identify relationships between sections and lines of code, using a text-based metamodeling framework [11] or a reference vocabulary, to identify potential candidates as groups of cohesive operations and associated resources. This should help in decomposing the monolith and also generate insights about granularity and cohesiveness of obtained microservices [7].…”

Section: Tab 4 Extraction Methods (Own Illustration)mentioning

confidence: 99%

Migrating Monoliths to Microservices

Bamberger¹,

Körber²

2023

JAMRIS

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This research should help scholars and practitioners to manage the transition of monolithic legacy application systems to microservices and to better understand the migration process, its steps, characteristics and provide guidance on how best to approach it. We performed a systematic literature review and analyzed migration approaches presented by other researches. We propose to leverage Robotic Process Automation technology to extract business logic, create and deploy bots, which are then used to mimic microservices. In essence, this represents a novel use case, integrating RPA technology into the migration approach in order to reduce uncertainty and risk of failure.

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Section: Tab 4 Extraction Methods (Own Illustration)mentioning

confidence: 99%

Migrating Monoliths to Microservices

Bamberger¹,

Körber²

2023

JAMRIS

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“…• https://github.com/HduDBSI/MsDecomposer [68] 2 https://bit.ly/publication-grouping • https://github.com/miguelfbrito/microservice-identification [55] • https://github.com/gmazlami/microserviceExtraction-backend [50] • https://github.com/socialsoftware/mono2micro [64] • https://github.com/antbucc/Migration [56]…”

Section: Publications Grouping and Selectionmentioning

confidence: 99%

Visually-Assisted Decomposition of Monoliths to Microservices

Salles,

Cunha

2023

2023 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)

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In the world of software development, the concept of microservices is popular. This architectural style has received much attention in both business and academia, and converting a monolithic application into a microservice-based application has become a regular practice. Companies struggle with migrating their existing monolithic applications to microservices, and software architects and developers frequently face challenges due to a lack of complete awareness of alternative migration methodologies, making the migration process even harder.This dissertation aims to structurally analyse the state of the art in migrating monolithic applications to microservices architectural style, mainly which tools help architects, engineers, and developers and how automated they are. A systematic literature review identified one hundred and six relevant publications. These publications were organised and grouped to provide a more comprehensive understanding of the current tools available for microservice migration.Furthermore, we present an extensible framework to help architects, engineers, and developers during the migration process by addressing gaps in understanding various migration tools and approaches, allowing for easy comparison between multiple options. The application combines multiple tools into one platform, allowing a comprehensive visualisation of migration proposals and making it easy to compare different options.To evaluate the efficacy of the application, we conducted an empirical study that focused on assessing its usability and the associated workload experienced by users during its utilisation. The study yielded favourable outcomes concerning usability, indicating that participants found the application user-friendly and intuitive. However, the workload results were diverse, implying that the application's efficacy in mitigating overall workload during the decomposition process was not universally experienced among the participants.

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“…The main difference between their research and ours is that they only facilitate the migration to MS architecture by generating some visual artifacts from Java annotations to help the developers understand the structure of the monolithic system, whereas our approach produces actual source code decomposition. In [7] the challenges of migration from monolithic applications to MS-based applications are tackled. In this paper, a model-driven approach is implemented utilizing JetBrains MPS (a text-based meta-modeling framework) for the automatic migration to MSs.…”

Section: Migration To Microservicesmentioning

confidence: 99%

Facilitating the migration to the microservice architecture via model-driven reverse engineering and reinforcement learning

et al. 2022

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The microservice architecture has gained remarkable attention in recent years. Microservices allow developers to implement and deploy independent services, so they are a naturally effective architecture for continuously deployed systems. Because of this, several organizations are undertaking the costly process of manually migrating their traditional software architectures to microservices.The research in this paper aims at facilitating the migration from monolithic software architectures to microservices. We propose a framework which enables software developers/architects to migrate their software systems more efficiently by helping them remodularize the source code of their systems. The framework leverages model-driven reverse engineering to obtain a model of the legacy system, and reinforcement learning to propose a mapping of this model towards a set of microservices.

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A Model-Driven Approach Towards Automatic Migration to Microservices

Cited by 13 publications

References 22 publications

Migrating Monoliths to Microservices

Migrating Monoliths to Microservices

Visually-Assisted Decomposition of Monoliths to Microservices

Facilitating the migration to the microservice architecture via model-driven reverse engineering and reinforcement learning

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