Automated Software Test Data Generation
IEEE Transactions on Software Engineering
Constraint-Based Automatic Test Data Generation
IEEE Transactions on Software Engineering
The nature of statistical learning theory
The nature of statistical learning theory
Making large-scale support vector machine learning practical
Advances in kernel methods
DAC '84 Proceedings of the 21st Design Automation Conference
Pseudo-oracles for non-testable programs
ACM '81 Proceedings of the ACM '81 conference
Real-time ranking with concept drift using expert advice
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Predicting electricity distribution feeder failures using machine learning susceptibility analysis
IAAI'06 Proceedings of the 18th conference on Innovative applications of artificial intelligence - Volume 2
Automatic generation of random self-checking test cases
IBM Systems Journal
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Automatic system testing of programs without test oracles
Proceedings of the eighteenth international symposium on Software testing and analysis
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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 those equivalence classes as parameters. This allows us to take advantage of randomness but still have control over test case selection at the system testing level. We present our findings from using the approach to test two different machine learning ranking applications.