Rafig Almaghairbe

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In recent years, software testing research has produced notable advances in the area of automated test data generation, but the corresponding oracle problem (a mechanism for determine the (in)correctness of an executed test case) is still a major problem. In this paper, we present a preliminary study which investigates the application of anomaly detection(More)
Developments in the automation of test data generation have greatly improved efficiency of the software testing process, but the so-called oracle problem (deciding the pass or fail outcome of a test execution) is still primarily an expensive and error-prone manual activity. We present an approach to automatically detect passing and failing executions using(More)
A key component of software testing is deciding whether a test case has passed or failed: an expensive and error-prone manual activity. We present an approach to automatically classify passing and failing executions using semi-supervised learning on dynamic execution data (test inputs/outputs and execution traces). A small proportion of the test data is(More)
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