Scalability and maintainability challenges in monolithic systems have led to the adoption of microservices, which divide systems into smaller, independent services. However, migrating existing monolithic systems to microservices is a complex and resource-intensive task, which can benefit from machine learning (ML) to automate some of its phases. Choosing the right ML approach for migration remains challenging for practitioners. Previous works studied separately the objectives, artifacts, techniques, tools, and benefits and challenges of migrating monolithic systems to microservices. No work has yet investigated systematically existing ML approaches for this migration to understand the automated migration phases, inputs used, ML techniques applied, evaluation processes followed, and challenges encountered.We present a systematic literature review (SLR) that aggregates, synthesises, and discusses the approaches and results of 81 primary studies (PSs) published between 2015 and 2024. We followed the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement to report our findings and answer our research questions (RQs).We extract and analyse data from these PSs to answer our RQs. We synthesise the findings in the form of a classification that shows the usage of ML techniques in migrating monolithic systems to microservices. The findings reveal that some phases of the migration process, such as monitoring and service identification, are well-studied, while others, like packaging microservices, remain unexplored. Additionally, the findings highlight key challenges, including limited data availability, scalability and complexity constraints, insufficient tool support, and the absence of standardized bench-marking, emphasizing the need for more holistic solutions.

Monolithic software systems face significant challenges in terms of maintenance, scalability, and portability. To address these challenges, many companies are embracing the microservices architectural style as a more flexible alternative to their monoliths. Microservices structure systems into modular, independent components, enabling easier development, deployment, and maintenance. However, the migration from a monolith to microservices is challenging due to the laborious task of manually identifying and decomposing a system into microservices. Several earlier studies focused on developing approaches to facilitate the migration process. However, the reliance on domain experts to define various parameters and thresholds restricted their use. In this paper, we introduce Magnet, a fully automated microservice identification approach, based on graph neural networks (GNNs). Magnet integrates a GNN model with a fine-grained method-based graph enriched with semantic and static features of the system. It enables accurate microservices identification while simultaneously promoting microservice cohesion and reducing microservice coupling. To validate the accuracy of Magnet, we performed extensive experiments using a set of open-source systems. Quantitatively, we use a set of quality metrics to assess the resulting microservices quality. We also compare our results to established ground truths. Empirical evidence suggests that our fully-automated approach Magnet achieves precision and recall rates of 56% and 68%. Qualitatively, we assess the modularity and functional independence of the resulting microservices by examining their relationships and semantic integrity. This evaluation demonstrates that our fully automated approach yields promising results, underlining its effectiveness in creating modular and coherent microservices.

Abstract 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.