Mono2Micro: A Practical and Effective Tool for Decomposing Monolithic Java Applications to Microservices
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Abstract (arXiv)
In migrating production workloads to cloud, enterprises often face the daunting task of evolving monolithic applications toward a microservice architecture. At IBM, we developed a tool called Mono2Micro to assist with this challenging task. Mono2Micro performs spatio-temporal decomposition, leveraging well-defined business use cases and runtime call relations to create functionally cohesive partitioning of application classes. Our preliminary evaluation of Mono2Micro showed promising results. How well does Mono2Micro perform against other decomposition techniques, and how do practitioners perceive the tool? This paper describes the technical foundations of Mono2Micro and presents results to answer these two questions. To answer the first question, we evaluated Mono2Micro against four existing techniques on a set of open-source and proprietary Java applications and using different metrics to assess the quality of decomposition and tool's efficiency. Our results show that Mono2Micro significantly outperforms state-of-the-art baselines in specific metrics well-defined for the problem domain. To answer the second question, we conducted a survey of twenty-one practitioners in various industry roles who have used Mono2Micro. This study highlights several benefits of the tool, interesting practitioner perceptions, and scope for further improvements. Overall, these results show that Mono2Micro can provide a valuable aid to practitioners in creating functionally cohesive and explainable microservice decompositions.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Jul 2021.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 0; 2025: 3.
4 papers.
- 2025Extracting Overlapping Microservices from Monolithic Code via Deep Semantic Embeddings and Graph Neural Network-Based Soft Clusteringcited by 1
- 2021An Expert System for Redesigning Software for Cloud Applicationscited by 0
- 2025MONO2REST: Identifying and Exposing Microservices: a Reusable RESTification Approachcited by 0
- 2025Identifying Appropriately-Sized Services with Deep Reinforcement Learningcited by 0
- 2025Identifying Appropriately-Sized Services with Deep Reinforcement Learning23 Dec 2025
- 2025Extracting Overlapping Microservices from Monolithic Code via Deep Semantic Embeddings and Graph Neural Network-Based Soft Clustering10 Aug 2025
- 2025MONO2REST: Identifying and Exposing Microservices: a Reusable RESTification Approach27 Mar 2025
- 2021An Expert System for Redesigning Software for Cloud Applications29 Sep 2021