A semantic mutation metric for metamorphic relation adequacy in scientific computing programs
Meng Li (1,2,3), Xiaohua Yang (1,2,3), Jie Liu (1,2,3), Shiyu Yan (1,2,3) ((1) School of Computing, University of South China, Hengyang, 421001, China (2) Hunan Engineering Research Center of Software Evaluation, Testing for Intellectual Equipment, Hengyang, 421001

TL;DR
This paper introduces the Semantic Mutation Score (SMS), a new domain-semantic mutation metric for evaluating metamorphic relations in scientific computing programs, addressing limitations of classical syntactic mutation scores.
Contribution
The paper proposes SMS, a semantic mutation metric built on five domain-specific operators, and demonstrates its effectiveness in assessing metamorphic relation adequacy in scientific computing.
Findings
SMS degenerates to classical MS in certain limits.
Cross-source pooling does not significantly affect the effect size.
Certain semantic mutation classes are unreachable with default syntactic configurations.
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 classical regime. Method. A 12-PUT x 5-MP design over four single-output float-to-float classes (numeric, probabilistic, surrogate, machine-learning) is paired with a three-layer attribution classifier separating true semantic faults from tolerance, OOD, statistical, and artefact categories. A same-source / cross-source ablation under an identical…
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