From Monolith To Microservices: A Comparative Evaluation of Decomposition Frameworks

Software modernisation through the migration from monolithic architectures to microservices has become increasingly critical, yet identifying effective service boundaries remains a complex and unresolved challenge. Although numerous automated microservice decomposition frameworks have been proposed, their evaluation is often fragmented due to inconsistent benchmark systems, incompatible metrics, and limited reproducibility, thus hindering objective comparison. This work presents a unified comparative evaluation of state-of-the-art microservice decomposition approaches spanning static, dynamic, and hybrid techniques. Using a consistent metric computation pipeline, we assess the decomposition quality across widely used benchmark systems JPetStore, AcmeAir, DayTrader, and Plants using Structural Modularity (SM), Interface Number (IFN), Inter-partition Communication (ICP), Non-Extreme Distribution (NED), and related indicators. Our analysis combines results reported in prior studies with experimentally reproduced outputs from available replication packages. Findings indicate that the hierarchical clustering-based methods, particularly HDBScan, produce the most consistently balanced decompositions across benchmarks, achieving strong modularity while minimizing communication and interface overhead. a-BMSC and Mono2Micro occupy subsequent performance tiers, exhibiting clear trade-offs between decomposition granularity and inter-service communication cost, while MEM demonstrates comparable behavior within this range. Embedding-based and graph neural network approaches show higher sensitivity to dataset characteristics, indicating a need for further methodological stabilisation.