Modernizing monolithic systems through microservices architectures (MSAs) promises significant benefits in terms of scalability, agility, and cloud adoption. However, this transition hinges on accurate and efficient microservices identification, a complex area of research still in its evolution. This systematic literature review delves into this challenge by exploring three critical questions: First, we examine how the field of microservices identification has evolved over time, analyzing publication trends, categorizing existing research, and mapping out different research objectives and methodologies employed. Second, we dive into the current state-of-the-art, showcasing cutting-edge methodologies and tools developed to address microservices identification challenges. We highlight promising approaches while identifying potential limitations. Third, we unveil both existing hurdles and future challenges in this domain, painting a comprehensive picture of the obstacles and opportunities that lie ahead. Our findings illuminate key areas demanding further attention, including the need for more automated and accurate identification tools, standardized evaluation benchmarks, and a deeper understanding of the human factors involved in successful transitions. By addressing these critical gaps, we aim to pave the way for smoother and more effective modernization of monolithic systems through microservices adoption.

The Microservice Architecture Style (MSA) has emerged as a significant computing paradigm in software engineering, with companies increasingly restructuring their monolithic systems to enhance digital performance and competitiveness. However, the migration process, particularly the microservice identification phase, presents complex challenges that require careful consideration. This study aimed to provide developers and researchers with a practical roadmap for microservice identification during legacy system migration while highlighting crucial migration steps and research requirements. Through a systematic mapping study following Kitchenham and Petersen’s guidelines, we analyzed various microservice identification approaches and developed a middleweight ontology that can be queried for key inputs, data modeling, identification algorithms, and performance evaluation metrics. Our research makes several significant contributions: a comprehensive analysis of existing identification methodologies, a multi-dimensional framework for categorizing and evaluating approaches, an examination of current research trajectories and literature gaps, an ontological framework specifically designed for microservice identification, and an outline of pressing challenges and future research directions. The study concluded that microservice identification remains a significant barrier in system migration efforts, highlighting the need for more research focused on developing effective identification techniques that consider various aspects, including roles and dependencies within a microservice architecture. This comprehensive analysis provides valuable insights for professionals and researchers working on microservice migration projects.

The rise of microservice architectures has further evolved software development practices, building upon the foundation established by web services in breaking down monolithic systems, while offering more finely detailed, coherent, and loosely interconnected services. Despite the transformative potential of microservices, the challenge of effectively pinpointing microservices that meet an organization’s specific requirements remains a difficult task. This paper presents an extended approach for automating microservices identification by leveraging business processes (BPs). Addressing limitations in existing microservices identification techniques, this approach utilizes advanced Natural Language Processing (NLP) techniques—such as Named Entity Recognition (NER) and semantic analysis—to capture dependencies and align boundaries with business logic. This methodology applies unsupervised clustering algorithms, including K-means, K-medoids, and DBSCAN, to generate well-defined microservices by analyzing semantic similarity across BP activities. Evaluation on real-world case studies, such as the Bicing bicycle rental system, Cargo Tracking, and JPetStore, demonstrates its effectiveness, yielding high cohesion, low coupling, and superior granularity in microservices decomposition compared to traditional methods. Metrics such as the Silhouette Index, Afferent Coupling, and Instability further validate its performance. By automating microservices identification with a BP-centered framework, this approach aligns technical architecture with organizational goals, advancing both agility and scalability in software systems. This work marks a step forward in optimizing system modularity and provides a foundation for future scalability improvements.