Goal-based Testing of Semantic Web Services

Abstract

Context: Recent years have witnessed growing interests in semantic web and its related technologies. While various frameworks have been proposed for designing semantic web services (SWS), few of them aim at testing. Objective: This paper investigates into the technologies for automatically deriving test cases from semantic web service descriptions based on the Web Service Modeling Ontology (WSMO) framework. Method: WSMO goal specifications were translated into B abstract machines. Test cases were generated via model checking with calculated trap properties from coverage criteria. Furthermore, we employed mutation analysis to evaluate the test suite. In this approach, the model-based test case generation and code-based evaluation techniques are independent of each other, which provides much more accurate measures of the testing results. Results: We applied our approach to a real-world case study of the Amazon E-Commerce Service (ECS). The experimental results have validated the effectiveness of the proposed solution. Conclusion: It is concluded that our approach is capable of automatically generating an effective set of test cases from the WSMO goal descriptions for SWS testing. The quality of test cases was measured in terms of their abilities to discover the injected faults at the code level. We implemented a tool to automate the steps for the mutation-based evaluation.