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Improving Dependability in Service Oriented Architectures using Ontologies and Fault Injection

Improving Dependability in Service Oriented Architectures using Ontologies and Fault Injection. Binka Gwynne Jie Xu School of Computing University of Leeds UK. Introduction and Objectives. Web services are the currently favoured middleware for Service Oriented Architectures

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Improving Dependability in Service Oriented Architectures using Ontologies and Fault Injection

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  1. Improving Dependability in Service Oriented Architectures using Ontologies and Fault Injection Binka Gwynne Jie Xu School of Computing University of Leeds UK

  2. Introduction and Objectives • Web services are the currently favoured middleware for Service Oriented Architectures • This middleware is continuously evolving due to increasing demands in requirements • It has yet to demonstrate capability of providing high levels of dependability • This dependability should be evident in order for Web services to become widely adopted in the real world

  3. Introduction and Objectives Existing fault injection tests are limited to manual comparisons We aim for evolutionary automated testing techniques that provide • Automated determination of dependability levels • Comparability metrics and novel evaluation techniques Our objective is to propose these innovative methods of testing Service Oriented Architecture middleware through the use of ontologically supported software fault injection

  4. 3 Elements of Research We combine 3 elements into our research study • Ontology • Experimental Provenance • Enhanced Fault Injection Testing

  5. 3 Elements of Research FAULT INJECTION TESTING TOOL ONTOLOGY TOOL EXPERIMENTAL PROVENANCE DATA These elements will be combined so that our ontology environment is able to cyclically evolve in complexity using information gleaned through fault injection testing

  6. Ontologies • An ontology is an abstract, formal and explicit description • Ontologies are abstractions that can be used to model the essence of complex phenomena • They can vary from flat lexicons with few relationships to very large, expressive ontologies attempting to capture every possible aspect of a domain • Languages and tools have been designed that assist in their development • Ontologies have been traditionally used in philosophy and science for a considerable time and their benefits are well proven

  7. Ontologically Supported Fault Injection Testing We aim to discover how faults affect systems by • injecting specifically designed faults • recording any resultant errors and if they propagate through to failures There are two main concerns with fault injection • Fault representativeness • The possibility of spawned faults

  8. Ontologically Supported Fault Injection Testing Using ontologically supported fault injection we aim to • Improve fault representativeness and avoid spurious testing • Lessen the chances of producing undetected spawned faults • Address problems associated with some traditional testing methods such as state and timing

  9. Experimental Data • Experimental results and provenance data are used to develop ontologies • This should allow every experiment to be replicable and tests to be made generally available to different systems • Information gathered should allow us to bring about improvements in further testing and evaluation procedures • Initial data is processed as XML documents • More detailed ontologies will require richer languages

  10. Example Experimental Data <test> <testID>Param0013</testID> <testerID>BG001</testerID> <ontologyEnv>null</ontologyEnv> <programName>Multi6Service</programName> <testDescription>parameter test started at Fri Aug 19 15:36:14 2005 using ex Service6</testDescription> <testType>parameter</testType> <testDate>[2005, 8, 19, 3, 36, 14, 1124462174313]</testDate> <testStartTime>1124462174313</testStartTime> <test> <testError> <errorDescription>parameter error in main line 107</errorDescription> <errorDetectionTime>1124462174746</errorDetectionTime> </testError> <testEndTime>1124462174972</testEndTime> </test>

  11. Conclusion Our ontology development is seen as a cyclic process where experimental results and provenance data are used in the design of new test and evaluation techniques This should mean that our experiments can be replicable and available to heterogeneous systems Our main objective is to make new methods available for the testing and evaluation of Service Oriented Architectures and Grid Systems

  12. For The Future • Our plans are to continue in developing novel methods for determining the dependability of Service Oriented Architectures and Grid systems by providing new criteria and metrics with the capability of evaluating dependability • We will refine the ontology engine so that it can become a valuable tool for the design and implementation of further test and evaluation methods

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