Changeability and evolvability analysis can aid an engineer tasked with a maintenance or an evolution task. This article applies change mining and evolution mining to evolving distributed systems. First, we propose a Service Change Classifier based Interface Slicing algorithm that mines change information from two versions of an evolving distributed system. To compare old and new versions, the following change classification labels are used: inserted, deleted, and modified. These labels are then used to identify subsets of the operations in our newly proposed Interface (WSDL) Slicing algorithm. Second, we proposed four Service Evolution Metrics that capture the evolution of a system's Version Series VS = {V₁, V₂,…,V_N}. Combined the two form the basis of our proposed Service Evolution Analytics model, which includes learning during its development phase. We prototyped the model in an intelligent tool named AWSCM (Automatic Web Service Change Management). Finally, we present results from experiments with two well-known cloud services: Elastic Compute Cloud (EC2) from the Amazon Web Service (AWS), and Cluster Controller (CC) from Eucalyptus. These experiments demonstrate AWSCM's ability to exploit change and evolution mining.

中文翻译：

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服务演化分析：分布式系统的变化与演化挖掘

可变性和进化性分析可以帮助负责维护或进化任务的工程师。本文适用换矿 和 进化挖掘不断发展的分布式系统。首先，我们提出一个服务变更分类器基于接口切片算法，该算法从不断发展的分布式系统的两个版本中挖掘更改信息。为了比较新旧版本，使用了以下更改分类标签：插入、删除和修改。然后，这些标签用于识别我们新提出的接口 (WSDL) 切片算法中的操作子集。其次，我们提出了四个服务演进指标 捕捉系统的演变 版本系列 VS = {伏₁、伏₂ ,…,V _ñ}. 将两者结合起来形成我们提出的基础服务演进分析模型，其中包括在其开发阶段的学习。我们在名为 AWSCM（自动 Web 服务变更管理）的智能工具中对模型进行了原型设计。最后，我们展示了两个著名云服务的实验结果：来自 Amazon Web Service (AWS) 的 Elastic Compute Cloud (EC2) 和来自 Eucalyptus 的集群控制器 (CC)。这些实验证明了 AWSCM 能够利用变化和进化挖掘。