Data generation in model-based testing
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In this paper we show how context free grammars extended with limited state information can be used to enhance model-based testing. Abstract models can be made more specific using the values generated by these grammars. The approach allows the association of data to the model without changing the model itself. We present two examples to illustrate the applicability of the framework. We also show how this approach is implemented in a real model-based testing tool.
This document has been peer reviewed.