Altalabani, Osama (2014) An automatic machine-learning framework for testing service-oriented architecure. (PhD thesis), Kingston University, .
Abstract
Today, Service Oriented Architecture (SOA) systems such as web services have the advantage of offering defined protocol and standard requirement specifications by means of a formal contract between the service requestor and the service provider, for example, the WSDL (Web Services Description Language) , PBEL (Business Process Execution Language), and BPMN (Business Process Model and Notation). This gives a high degree of flexibility to the design, development, Information Technology (IT) infrastructure implementation, and promise a world where computing resources work transparently and efficiently. Furthermore, the rich interface standards and specifications of SOA web services (collectively referred to as the WS-* Architecture) enable service providers and consumers to solve important problems, as these interfaces enable the development of interoperable computing environments that incorporate end-to-end security, reliability and transaction support, thus, promoting existing IT infrastructure investments. However, many of the benefits of SOA become challenges for testing approaches and frameworks due to their specific design and implementation characteristics, which cause many testability problems. Thus, a number of testing approaches and frameworks have been proposed in the literature to address various aspects of SOA testability. However, most of these approaches and frameworks are based on intuition and not carried out in a systematic manner that is based on the standards and specifications of SOA. Generally, they lack sophisticated and automated testing, which provide data mining and knowledge discovery in accordance with the system based on SOA requirements, which consequently would provide better testability, deeper intelligence and prudence. Thus, this thesis proposes an automated and systematic testing framework based on user requirements, both functional and non-functional, with support of machine-learning techniques for intelligent reliability, real-time monitoring, SOA protocols and standard requirements coverage analysis to improve the testability of SOA-based systems. This thesis addresses the development, implementation, and evaluation of the proposed framework, by means of a proof-of-concept prototype for testing SOA systems based on the web services protocol stack specifications. The framework extends to intelligent analysis of SOA web service specifications and the generation of test cases based on static test analysis using machine-learning support.
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