Ontology for service oriented testing of web services

This paper presents a service oriented architecture for testing Web Services. In this architecture, various parties interoperate with each other to complete testing tasks through testing service registration, discovery and invocation. The analysis of the architecture in a typical scenario shows that it has the advantages of supporting dynamic discovery and invocation of testing services as required by the dynamic discovery and invocation of normal functional services without compromising security, privacy and intellectual property rights. It is flexibleand extendable. It also helps to reduce the risk ofunnecessary disturbances to the normal operations of services due to testing activities. The paper reports a prototype implementation of the architecture by adapting and implementing the ontology of software testing using Semantic Web Services technology. A case studywith the WS wrapping of an automated testing tool is also reported, which demonstrated that the architecture is technically feasible

Preemptive regression testing of workflow-based web services

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Automated Realistic Test Input Generation and Cost Reduction in Service-centric System Testing

Service-centric System Testing (ScST) is more challenging than testing traditional software due to the complexity of service technologies and the limitations that are imposed by the SOA environment. One of the most important problems in ScST is the problem of realistic test data generation. Realistic test data is often generated manually or using an existing source, thus it is hard to automate and laborious to generate. One of the limitations that makes ScST challenging is the cost associated with invoking services during testing process. This thesis aims to provide solutions to the aforementioned problems, automated realistic input generation and cost reduction in ScST. To address automation in realistic test data generation, the concept of Service-centric Test Data Generation (ScTDG) is presented, in which existing services used as realistic data sources. ScTDG minimises the need for tester input and dependence on existing data sources by automatically generating service compositions that can generate the required test data. In experimental analysis, our approach achieved between 93% and 100% success rates in generating realistic data while state-of-the-art automated test data generation achieved only between 2% and 34%. The thesis addresses cost concerns at test data generation level by enabling data source selection in ScTDG. Source selection in ScTDG has many dimensions such as cost, reliability and availability. This thesis formulates this problem as an optimisation problem and presents a multi-objective characterisation of service selection in ScTDG, aiming to reduce the cost of test data generation. A cost-aware pareto optimal test suite minimisation approach addressing testing cost concerns during test execution is also presented. The approach adapts traditional multi-objective minimisation approaches to ScST domain by formulating ScST concerns, such as invocation cost and test case reliability. In experimental analysis, the approach achieved reductions between 69% and 98.6% in monetary cost of service invocations during testin