Parameterizing random test data according to equivalence classes

@inproceedings{Murphy2007ParameterizingRT,
  title={Parameterizing random test data according to equivalence classes},
  author={Christian Murphy and Gail Kaiser and Marta Arias},
  booktitle={RT '07},
  year={2007}
}
We are concerned with the problem of detecting bugs in machine learning applications. In the absence of sufficient real-world data, creating suitably large data sets for testing can be a difficult task. To address this problem, we have developed an approach to creating data sets called "parameterized random data generation". Our data generation framework allows us to isolate or combine different equivalence classes as desired, and then randomly generate large data sets using the properties of… CONTINUE READING

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