Microservices Backlog - A Model of Granularity Specification and Microservice Identification

Vera-Rivera, F. H.; Cuadros, Eduard Gilberto Puerto; Astudillo, Hernán; Gaona, Mauricio

doi:10.1007/978-3-030-59592-0_6

Cited by 12 publications

(6 citation statements)

References 19 publications

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“…The graph theory of design properties enables the direct analysis of coupling properties. Thirteen metrics have been proposed for measuring microservice coupling [35], [43], [44], [54], [59], [60], while fourteen metrics for service-oriented architecture (SOA) [28], [51], [58], [61]. Expressively, four of the proposed microservice metrics by Bogner et al [29] were derived from SOA metrics [53] in the context of microservice architecture.…”

Section: A Couplingmentioning

confidence: 99%

From Monolith to Microservice: Measuring Architecture Maintainability

Hasan¹,

Osman²,

Admodisastro³

et al. 2023

IJACSA

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The migration of monolithic applications to the cloud is a popular trend, with microservice architecture being a commonly targeted architectural pattern. The motivation behind this migration is often rooted in the challenges associated with maintaining legacy applications and the need to adapt to rapidly changing business requirements. To ensure that the migration to microservices is a sound decision for enhancing maintainability, designers must carefully consider the underlying factors driving this software architecture migration. This study proposes a set of software architecture metrics for evaluating the maintainability of microservice architectural designs for monolith to microservice architecture migration. These metrics consider various factors, such as coupling, complexity, cohesion, and size, which are crucial for ensuring that the software architecture remains maintainable in the long term. Drawing upon previous product quality models that share similar design properties with microservice, we have derived maintainability metrics that can help measure the quality of microservice architecture. In this work, we introduced our first version of structural metrics for measuring the maintainability quality of microservice architecture concerning its cloud-native characteristics. This work allows us to get early feedback on proposed metrics before a detailed evaluation. With these metrics, designers can measure their microservice architecture quality to fully leverage the benefits of the cloud environment, thus ensuring that the migration to microservice is a beneficial decision for enhancing the maintainability of their software architecture applications.

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Section: A Couplingmentioning

confidence: 99%

From Monolith to Microservice: Measuring Architecture Maintainability

Hasan¹,

Osman²,

Admodisastro³

et al. 2023

IJACSA

View full text Add to dashboard Cite

show abstract

“…The genetic algorithm considers coupling, complexity, semantic similarity and cohesion metrics to automatically distribute user stories to microservices. It tries to find the best combination and assignment of user stories to microservices, thus minimizing the granularity metric ( Vera-Rivera et al, 2020 ). It is important to note that semantics and conceptual similarity play a big role in several topics around architecture, including architecture recovery, refactoring ( e.g ., Feature Envy, Move Method, Extract Class), and design principles (single responsibility principle).…”

Section: Microservices Backlogmentioning

confidence: 99%

SEMGROMI—a semantic grouping algorithm to identifying microservices using semantic similarity of user stories

Vera-Rivera

Cuadros

Pérez

et al. 2023

PeerJ Computer Science

Self Cite

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Microservices is an architectural style for service-oriented distributed computing, and is being widely adopted in several domains, including autonomous vehicles, sensor networks, IoT systems, energy systems, telecommunications networks and telemedicine systems. When migrating a monolithic system to a microservices architecture, one of the key design problems is the “microservice granularity definition”, i.e., deciding how many microservices are needed and allocating computations among them. This article describes a semantic grouping algorithm (SEMGROMI), a technique that takes user stories, a well-known functional requirements specification technique, and identifies number and scope of candidate microservices using semantic similarity of the user stories’ textual description, while optimizing for low coupling, high cohesion, and high semantic similarity. Using the technique in four validation projects (two state-of-the-art projects and two industry projects), the proposed technique was compared with domain-driven design (DDD), the most frequent method used to identify microservices, and with a genetic algorithm previously proposed as part of the Microservices Backlog model. We found that SEMGROMI yields decompositions of user stories to microservices with high cohesion (from the semantic point of view) and low coupling, the complexity was reduced, also the communication between microservices and the estimated development time was decreased. Therefore, SEMGROMI is a viable option for the design and evaluation of microservices-based applications. The proposed semantic similarity-based technique (SEMGROMI) is part of the Microservices Backlog model, which allows to evaluate candidate microservices graphically and based on metrics to make design-time decisions about the architecture of the microservices-based application.

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“…Their multimodel approach combines different independent models that represent a business process like control, data, and semantic dependencies [16]. Rivera et al [17] propose another intelligent and optimal method that works on user stories to decompose the functionalities or requirements of the application into microservices. Most of these existing approaches have two important disadvantages: 1) Lack of validation of the results and 2) Over dependency of their approach on expert opinion.…”

Section: Related Workmentioning

confidence: 99%

GreenMicro: Identifying Microservices From Use Cases in Greenfield Development

2022

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Microservices architecture is a new paradigm for developing a software system as a collection of independent services that communicate through lightweight protocols. In greenfield development, identifying the microservices is not a trivial task, as there is no legacy code lying around and no old development to start with. Thus, identification of microservices from requirements becomes an important decision during the analysis and design phase. Use cases play a vital role in the requirements analysis modeling phases in a model-driven software engineering process. It is a technique of capturing high-level user functions and scope of the system. In this paper, we propose GreenMicro, an automatic microservice identification technique that utilizes the use cases model and the database entities. Both features are the artifacts of analysis and design phase that depict complete functionality of an overall system. In essence, a collection of related use cases indicates a bounded context of the system that can be grouped in a suitable way as microservices. Therefore, our approach GreenMicro clusters close-knit use cases to recover meaningful microservices. We investigate and validate our approach on an in-house proprietary web application and three sample benchmark applications. We have mapped our approach to state-of-the-art software quality assessment attributes and have presented the results. Preliminary results are motivating and the proposed methodology works as anticipated in producing functionally cohesive and loosely coupled microservice candidate recommendations. Our approach enables the system architects to identify microservice candidates at an early analysis and design phase of development.

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Microservices Backlog - A Model of Granularity Specification and Microservice Identification

Cited by 12 publications

References 19 publications

From Monolith to Microservice: Measuring Architecture Maintainability

From Monolith to Microservice: Measuring Architecture Maintainability

SEMGROMI—a semantic grouping algorithm to identifying microservices using semantic similarity of user stories

GreenMicro: Identifying Microservices From Use Cases in Greenfield Development

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