Towards Automated Microservices Extraction Using Muti-objective Evolutionary Search

Saidani, Islem; Ouni, Ali; Mkaouer, Mohamed Wiem; Saied, Aymen

doi:10.1007/978-3-030-33702-5_5

Cited by 23 publications

(30 citation statements)

References 8 publications

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“…Most papers that used static analysis as their main method tended to do so to gather information specifically about the architecture. These techniques depend on analyzing statically defined artifacts to reconstruct an architectural view of a system, mainly analyzing source code [16,26,28,30], but also other artifacts, such as OpenAPI specifications [15]. Static analysis is applicable to other goals as well, such as anti-pattern or code smell detection [26,28,29].…”

Section: Rq1: Methods and Techniques Usedmentioning

confidence: 99%

“…Then each candidate microservice, one per reference concept, is defined by its operations and their parameters, complex types, and return values. Saidani et al [16] use static analysis to identify candidate microservices while considering structural dependencies of the source code. First, an empty set of microservices is created.…”

Section: Static Analysismentioning

confidence: 99%

“…For a similar purpose, Walker et al developed a new tool MSANose [27] that can identify 11 microservice-specific code smells. The tool MSExtractor was presented by Saidani et al [16] to decompose monolithic applications into microservices. Kleehaus et al introduced the tool MICROLYZE, which can recover software architecture by mapping infrastructure information with business processes.…”

Section: Toolsmentioning

confidence: 99%

“…Baresi et al [15] aim to aid in identifying potential microservice candidates by using OpenAPI specifications and reference vocabulary to identify potential candidate microservices. Saidani et al [16] introduce MSExtractor, a novel approach that uses a non-dominated sorting genetic algorithm (NSGA-II) to decompose an object-oriented application into cohesive, loosely coupled microservices. This tool uses static-code analysis to determine a decomposition that is optimized with respect to two objective functions: cohesion and coupling [24].…”

Section: Migration To Microservicesmentioning

confidence: 99%

“…Another common topic addressed was migration to microservice-based architectures either by providing tools to decompose monolithic architectures into microservices [15,16,19,32,50,56] or to assist in deciding if migration is beneficial and feasible [21,48,49,64]. Although we could assume a silver bullet by now when it comes to the design of microservice systems, there is no perfect guidance for engineers managing legacy systems.…”

Section: Rq2: Goals Addressed By the Papersmentioning

confidence: 99%

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On Microservice Analysis and Architecture Evolution: A Systematic Mapping Study

et al. 2021

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Microservice architecture has become the leading design for cloud-native systems. The highly decentralized approach to software development consists of relatively independent services, which provides benefits such as faster deployment cycles, better scalability, and good separation of concerns among services. With this new architecture, one can naturally expect a broad range of advancements and simplifications over legacy systems. However, microservice system design remains challenging, as it is still difficult for engineers to understand the system module boundaries. Thus, understanding and explaining the microservice systems might not be as easy as initially thought. This study aims to classify recently published approaches and techniques to analyze microservice systems. It also looks at the evolutionary perspective of such systems and their analysis. Furthermore, the identified approaches target various challenges and goals, which this study analyzed. Thus, it provides the reader with a roadmap to the discipline, tools, techniques, and open challenges for future work. It provides a guide towards choices when aiming for analyzing cloud-native systems. The results indicate five analytical approaches commonly used in the literature, possibly in combination, towards problems classified into seven categories.

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Section: Rq1: Methods and Techniques Usedmentioning

confidence: 99%

Section: Static Analysismentioning

confidence: 99%

Section: Toolsmentioning

confidence: 99%

Section: Migration To Microservicesmentioning

confidence: 99%

Section: Rq2: Goals Addressed By the Papersmentioning

confidence: 99%

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On Microservice Analysis and Architecture Evolution: A Systematic Mapping Study

et al. 2021

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From legacy to microservices: A type‐based approach for microservices identification using machine learning and semantic analysis

Trabelsi

Abdellatif

Abubaker

et al. 2022

J Software Evolu Process

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The microservices architecture (MSA) style has been gaining interest in recent years because of its high scalability, ability to be deployed in the cloud, and suitability for DevOps practices. While new applications can adopt MSA from their inception, many legacy monolithic systems must be migrated to an MSA to benefit from the advantages of this architectural style. To support the migration process, we propose MicroMiner, a microservices identification approach that is based on static‐relationship analyses between code elements as well as semantic analyses of the source code. Our approach relies on machine learning (ML) techniques and uses service types to guide the identification of microservices from legacy monolithic systems. We evaluate the efficiency of our approach on four systems and compare our results to ground‐truths and to those of two state‐of‐the‐art approaches. We perform a qualitative evaluation of the resulted microservices by analyzing the business capabilities of the identified microservices. Also a quantitative analysis using the state‐of‐the‐art metrics on independence of functionality and modularity of services was conducted. Our results show the effectiveness of our approach to automate one of the most time‐consuming steps in the migration of legacy systems to microservices. The proposed approach identifies architecturally significant microservices with a 68.15% precision and 77% recall.

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