不透明响应生成支持自动创建虚拟服务以实现服务虚拟化
Opaque Response Generation Enabling Automatic Creation of Virtual Services for Service Virtualisation
摘要：
服务虚拟化是一种创建虚拟服务模型的方法，该模型可以模拟被测系统与目标系统之间的交互行为。通过服务虚拟化，开发团队可以随时随地访问类似生产的条件，从而实现频繁和全面的测试。以前的服务虚拟化技术依赖于服务专家对目标服务进行显式建模，并需要详细的消息协议和结构知识。然而，这两种都不一定可用。在这篇论文中，我们介绍了我们的新的不透明响应生成方法。这种方法使服务能够自动虚拟化，而无需任何系统协议和交互行为的专业知识或文档。给定被测系统与目标真实服务之间的交互集合，我们的方法可以：1）将相同类型的交互组织到同一个簇中，并为每个簇导出一个簇原型; 2）在交互库中搜索给定的传入请求，以寻找与之最相似的请求; 3）从传入请求和记录的交互中学习知识;以及4）生成响应。我们的不透明的响应生成方法的框架和概念验证的实现进行了描述。实验结果表明，我们的不透明的响应生成方法是能够自动生成准确的响应，在实时的，平均准确率超过99%。
英文摘要：
Service virtualisation is a method to create virtual service models that can mimic interaction behaviors between a system under test and the target system. With service virtualisation, the development team can get access to the production-like conditions whenever and however many times they need, enabling frequent and comprehensive testing. Previous techniques for service virtualisation have relied on explicitly modelling the target services by a service expert and require detailed knowledge of message protocol and structure. However, neither of these are necessarily available. In this thesis, we introduce our novel opaque response generation approach. This approach enables services to be virtualised automatically without any expert knowledge or documentation of system protocol and interaction behaviours. Given a collection of interactions exchanged between a system under test and a target real service, our approach can 1) organise the same type of interactions into the same cluster and derive a cluster prototype for each cluster; 2) search a given incoming request for its the most similar request in the interaction library; 3) learn knowledge from the incoming request and the recorded interaction; and 4) generate a response. A framework and proof-of-concept implementation of our opaque response generation approach is described. Experimental results show our opaque response generation approach is able to automatically generate accurate responses in real time with an accuracy rate over 99% on average.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
