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Test targets were also introduced that help to control and measure the effectiveness of test generation, traceability techniques for relating the informal requirements to the generated tests, and techniques for designing nondeterministic models of nondeterministic systems.
Using model checking and execution generated tests to find bugs in real code (postscript, PDF, PPT).
The automatically generated tests, which are used in our assessment, are based on the RDFS/OWL (Web Ontology Language) schema of the assessed dataset.
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Distributions for the other random variables were obtained using previously generated test data.
Non-linear finite element models were developed and validated against the generated test data.
The performance of the developed models was compared among themselves for 4 randomly generated test cases.
The performance of all developed algorithms are then analysed using randomly generated test instances.
The effectiveness of our approach is demonstrated on a set of randomly generated test data.
A technical report containing our SOSP05 submission gives preliminary results: Execution generated test cases: how to make systems code crash itself, CSTR-2005-04 (pdf).
Computational tests carried out on randomly generated test instances indicate that the proposed hybrid approach is both accurate and efficient.
In addition, randomly generated test problems were also used to investigate the performance of the hybrid heuristic approaches.
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CEO of Professional Science Editing for Scientists @ prosciediting.com