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.

翻译：通过从单体架构迁移至微服务架构的软件现代化已变得日益关键，然而识别有效的服务边界仍然是一项复杂且尚未解决的挑战。尽管已提出众多自动化微服务分解框架，但由于基准系统不一致、度量指标不兼容以及可复现性有限，其评估往往呈现碎片化，从而阻碍了客观比较。本研究对涵盖静态、动态及混合技术的最先进微服务分解方法进行了统一的比较评估。通过采用一致的度量计算流程，我们使用结构模块性（SM）、接口数量（IFN）、分区间通信（ICP）、非极端分布（NED）及相关指标，在广泛使用的基准系统（JPetStore、AcmeAir、DayTrader 和 Plants）上评估了分解质量。我们的分析结合了先前研究报告的结果与通过可用复现包实验重现的输出。研究结果表明，基于层次聚类的方法（尤其是 HDBScan）能在各基准测试中产生最一致且平衡的分解方案，在实现强模块性的同时最小化通信与接口开销。