A semantic mutation metric for metamorphic relation adequacy in scientific computing programs
arXiv:2605.17437v2 Announce Type: replace-cross Abstract: Context. Metamorphic Testing addresses the test-oracle problem in scientific computing, but classical Mutation Score operates on syntactic AST mutations and misses domain semantics. Objective. We propose the Semantic Mutation Score (SMS), built on five domain-semantic operators (Conservation Erosion, Operator Substitution, Hyperparameter, Trajectory Flip, Structural Injection). SMS degenerates almost everywhere to MS in a characterised limit, so any SMS-based conclusion remains consistent with prior mutation-testing literature in the cl
The increasing complexity and domain specificity of AI and scientific computing programs necessitate more sophisticated testing methodologies beyond syntactic checks.
This development addresses a critical limitation in software testing for scientific computing and AI, enabling more robust and reliable system development.
Testing protocols for complex AI and scientific computing applications can now incorporate domain-semantic understanding, improving test coverage and reliability.
- · AI/ML developers
- · Scientific computing researchers
- · Software quality assurance industry
- · High-stakes AI applications
- · Traditional syntactic mutation testing tools
- · Organizations with inadequate testing practices
Improved reliability and trustworthiness of AI models in scientific and critical applications.
Faster adoption of AI in domains requiring high assurance due to enhanced testing capabilities.
Potential for new regulatory frameworks for AI and scientific software to mandate semantic testing standards.
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Read at arXiv cs.LG