Predicting metamorphic relations for testing scientific software: a machine learning approach using graph kernels

@article{Kanewala2016PredictingMR,
  title={Predicting metamorphic relations for testing scientific software: a machine learning approach using graph kernels},
  author={Upulee Kanewala and James M. Bieman and Asa Ben-Hur},
  journal={Softw. Test., Verif. Reliab.},
  year={2016},
  volume={26},
  pages={245-269}
}
Comprehensive, automated software testing requires an oracle to check whether the output produced by a test case matches the expected behavior of the program. But the challenges in creating suitable oracles limit the ability to perform automated testing in some programs, and especially in scientific software. Metamorphic testing is a method for automating the testing process for programs without test oracles. This technique operates by checking whether the program behaves according to… CONTINUE READING
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Predicting Metamorphic Relations for Matrix Calculation Programs

2018 IEEE/ACM 3rd International Workshop on Metamorphic Testing (MET) • 2018

Prioritization of Metamorphic Relations Based on Test Case Execution Properties

2018 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) • 2018

Using Semi-Supervised Learning for Predicting Metamorphic Relations

2018 IEEE/ACM 3rd International Workshop on Metamorphic Testing (MET) • 2018
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