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

Author:

Kanewala Upulee1,Bieman James M.2,Ben-Hur Asa2

Affiliation:

1. Computer Science Department; Montana State University; Bozeman MT USA

2. Computer Science Department; Colorado State University; Fort Collins CO USA

Funder

National Institute Of General Medical Sciences

Publisher

Wiley

Subject

Safety, Risk, Reliability and Quality,Software

Reference55 articles.

1. Dealing with risk in scientific software development;Sanders;IEEE Software,2008

2. The T experiments: errors in scientific software;Hatton;IEEE Computational Science Engineering,1997

3. A case study on testing CMM uncertainty simulation software (VCMM);Abackerli;Journal of the Brazilian Society of Mechanical Sciences and Engineering,2010

4. A scientist's nightmare: software problem leads to five retractions;Miller;Science,2006

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