Abstract : We present an application of learning-based testing
to the problem of automated test case generation (ATCG) for numerical
software. Our approach uses n-dimensional polynomial models as an
algorithmically learned abstraction of the SUT which supports n-wise
testing. Test cases are iteratively generated by applying a
satisfiability algorithm to first-order program specifications over real
closed fields and iteratively refined piecewise polynomial models. We
benchmark the performance of our iterative ATCG algorithm against
iterative random testing, and empirically analyse its performance in
finding injected errors in numerical codes. Our results show that for
software with small errors, or long mean time to failure, learning-based
testing is increasingly more efficient than iterative random
testing.
https://hal.inria.fr/hal-01055250 Contributor : Hal IfipConnect in order to contact the contributor Submitted on : Tuesday, August 12, 2014 - 9:14:26 AM Last modification on : Wednesday, August 16, 2017 - 3:22:41 PM Long-term archiving on: : Wednesday, November 26, 2014 - 10:37:09 PM
Karl Meinke, Fei Niu. A Learning-based Approach to Unit Testing of
Numerical Software. 22nd IFIP WG 6.1 International Conference on Testing Software and Systems (ICTSS), Nov 2010, Natal, Brazil. pp.221-235, ⟨10.1007/978-3-642-16573-3_16⟩. ⟨hal-01055250⟩