The foundations of program verification (2nd ed.)
The foundations of program verification (2nd ed.)
How Accurate is Scientific Software?
IEEE Transactions on Software Engineering
Automating Specification-Based Software Testing
Automating Specification-Based Software Testing
FMCAD '02 Proceedings of the 4th International Conference on Formal Methods in Computer-Aided Design
SAT Based Abstraction-Refinement Using ILP and Machine Learning Techniques
CAV '02 Proceedings of the 14th International Conference on Computer Aided Verification
Automated black-box testing of functional correctness using function approximation
ISSTA '04 Proceedings of the 2004 ACM SIGSOFT international symposium on Software testing and analysis
Building PDE Codes to be Verifiable and Validatable
Computing in Science and Engineering
The Chimera of Software Quality
Computer
Incremental learning-based testing for reactive systems
TAP'11 Proceedings of the 5th international conference on Tests and proofs
Learning-based testing for reactive systems using term rewriting technology
ICTSS'11 Proceedings of the 23rd IFIP WG 6.1 international conference on Testing software and systems
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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.