NOETHER: A Constructive Framework for Metamorphic Pattern Discovery from Operator Algebras
For software testing researchers and practitioners, NOETHER shifts MR identification from per-program inductive sampling to a per-domain algebraic deduction, but the approach is incremental as it builds on existing MetaPattern catalogues and requires domain-specific algebraic curation.
The paper addresses the bottleneck of metamorphic relation (MR) identification in metamorphic testing by proposing NOETHER, a constructive framework that derives MetaPattern sets from program-induced operator algebras with algebraic closure and polynomial-time decidability guarantees. The framework systematizes prior catalogues in Boltzmann reactor physics, derives executable MRs for equivariant ML, and exercises relational-equivalence on query optimisers, while falsifying an absolute-completeness conjecture via counterexamples.
Context. Metamorphic Testing is recognised in IEEE/ISO software-testing standards and increasingly recommended for AI systems, but its progress is bottlenecked by metamorphic relation (MR) identification: existing approaches (structured frameworks, mining and evolutionary pipelines, LLM-assisted methods, MetaPattern catalogues) share an inductive grounding that leaves three foundational questions open: origin, closure, and transferability. Objective. We propose a framework whose downstream step from program-induced operator algebra to MetaPattern set is mechanical and provable, while the upstream curation of the algebra is a stated empirical hypothesis with explicit scope precondition. Method. NOETHER is a two-layer framework. The upstream layer is an eight-block decomposition over recurrent mathematical structures (symmetry, order, self-adjoint, time-reversal, limit, qualitative-dynamics, method-comparison, relational equivalence). The downstream CONSTRUCT-MP algorithm produces a MetaPattern set with algebraic-closure (Theorem 1) and polynomial-time decidability (Theorem 2) guarantees. We test the framework on three operator-algebraic domains. Results. On Boltzmann reactor physics NOETHER systematises a prior inductive catalogue; on equivariant ML it derives executable MRs for rotation invariance, adjoint duality, and training-trajectory reversibility; on relational query optimisers it exercises the relational-equivalence block. The central falsifiable prediction (L*-blindness on homogeneity-preserving mutators) holds on the in-scope substrate. The absolute-completeness conjecture (Theorem 1') is falsified on PWR core diffusion via two pairwise-independent counterexamples that identify five Translate-extension dimensions. Conclusion. Induction is relocated from per-program MR sampling to a per-domain algebraic layer; the downstream step is deductive and mechanical.