TripleBound：基于三元组引导的异构图学习的微服务分解方法
TripleBound: Triplet-Guided Heterogeneous Graph Learning for Microservice Decomposition
- Department of Computer Science and Engineering, University of Moratuwa（莫拉图瓦大学计算机科学与工程系）
- WSO2 LLC（WSO2有限公司）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对单体到微服务分解中结构依赖与语义信号未统一优化的问题，提出TripleBound框架，利用三元组约束增强异构图神经网络，在多个基准上取得领先的综合分解得分。
AI中文摘要：
云计算和DevOps使得微服务成为构建可扩展、可维护软件系统的常见架构。然而，由于紧耦合和模糊的服务边界，将单体应用迁移到微服务仍然具有挑战性。现有的分解方法通常依赖于结构依赖或语义相似性信号，但很少在统一的表示学习目标中同时整合两者。本文提出了TripleBound，一个用于自动化单体到微服务分解的混合框架，该框架通过基于包结构、命名约定和代码位置的解析器推断服务组，生成弱监督三元组约束，并增强异构图神经网络。TripleBound将基于三元组的约束直接注入共享的结构潜在空间，使得两种信号在表示学习过程中能够被联合优化。结构依赖通过CHGNN捕获，CHGNN将单体建模为包含程序节点、资源节点、CALL边和CRUD边的异构图。语义关系通过从解析器推断的服务组生成的三元组约束来整合。在AcmeAir、DayTrader、PlantsByWebSphere和JPetStore上的评估表明，与CHGNN和MonoEmbed相比，在选定的权重下，TripleBound在AcmeAir、DayTrader和JPetStore上取得了最高的综合分解得分，而CHGNN在PlantsByWebSphere上仍然更强。逐指标分析揭示了权衡：结构模块化和分区间耦合的增益伴随着某些数据集上更高的实体分布不平衡。替代的综合权重保持了TripleBound在AcmeAir和DayTrader上的第一名排名，但在JPetStore上则不然，这表明总体排名依赖于指标。
英文摘要：
Cloud computing and DevOps have made microservices a common architecture for scalable, maintainable software systems. However, migrating monoliths to microservices remains challenging due to tight coupling and unclear service boundaries. Existing decomposition approaches typically rely on either structural dependencies or semantic similarity signals, but rarely integrate both within a unified representation learning objective. This paper proposes TripleBound, a hybrid framework for automated monolith-to-microservices decomposition that augments a heterogeneous graph neural network with weakly supervised triplet constraints derived from parser-inferred service groups based on package structure, naming conventions, and code location. TripleBound injects triplet-based constraints directly into the shared structural latent space, enabling both signals to be jointly optimized during representation learning. Structural dependencies are captured using CHGNN, which models the monolith as a heterogeneous graph with program nodes, resource nodes, CALL edges, and CRUD edges. Semantic relationships are incorporated through triplet constraints generated from parser-inferred service groups. Evaluation on AcmeAir, DayTrader, PlantsByWebSphere, and JPetStore shows that TripleBound achieves the highest composite decomposition score under the selected weighting on AcmeAir, DayTrader, and JPetStore compared to CHGNN and MonoEmbed, while CHGNN remains stronger on PlantsByWebSphere. Per-metric analysis reveals trade-offs: gains in structural modularity and inter-partition coupling are accompanied by higher entity distribution imbalance on some datasets. Alternative composite weightings preserve TripleBound's first-place ranking on AcmeAir and DayTrader but not on JPetStore, showing that the aggregate ranking is metric-dependent.
