Metamorphic Relations (MRs) in Software Testing

• Metamorphic Relations (MRs) are formal properties that specify invariant relationships across multiple test executions for effective fault detection.
• They generate follow-up test cases by applying systematic input transformations, serving as relational oracles in testing scenarios.
• MRs are widely used in domains from machine learning to smart contracts, enhancing testing automation where traditional oracles fall short.

Metamorphic Relations (MRs) are formal properties that specify relationships among input–output pairs over multiple executions of a system under test (SUT). As foundational artifacts in the metamorphic testing (MT) paradigm, MRs address the oracle problem—the difficulty or impossibility of defining explicit expected outputs for complex, data-driven, or nondeterministic systems—by providing relational oracles. MRs are used to generate and relate “source” and “follow-up” test cases, defining how the output must (invariantly or predictably) change in response to systematic input transformations. Violations of MRs serve as proxies for implementation bugs, faults, or other software defects in the absence of traditional oracles.

1. Foundational Concepts and Formalism

A metamorphic relation is typically formulated as a necessary property of a program’s underlying function. If $p$ implements $f: X \rightarrow Y$, then an MR $\mathcal{R} \subseteq X^n \times Y^n$ relates $n$ distinct test inputs (at least one being the “source,” others as “follow-up”) and their outputs:

$$\mathcal{R}(t_1, t_2, \ldots, t_n, f(t_1), f(t_2), \ldots, f(t_n))$$

MRs are often decomposed into input relations ($R_{in}$) and output relations ($R_{out}$), with the general semantic form:

$$\text{If } R_{in}(t_1, t_2, \ldots, t_n) \text{ then } R_{out}(f(t_1), f(t_2), \ldots, f(t_n))$$

Common types include invariance (e.g., outputs unchanged under input permutation), equivalence under transformation (e.g., $f(x) = f(-x)$ for even functions), scaling, or more complex domain-specific properties. In software systems without clear oracles (e.g., ML classifiers, Web systems, smart contracts), MRs serve as the test oracle by specifying testable properties that must hold regardless of specific input values.

2. Systematic Identification, Expression, and Classification of MRs

Identifying high-quality MRs is a non-trivial process requiring domain, algorithmic, or specification-derived knowledge. Several systematic approaches are described:

• Category-Choice and IO-CTF Frameworks: Identify program parameters and partition input/output domains into categories/choices. Candidate MR pairs are generated by combining IO-Complete Test Frames (IO-CTFs), first inferring $R_{out}$ from the outputs, and then deducing $R_{in}$ (Fu et al., 30 Dec 2024).
• Control-Theoretical and Mathematical Properties: In domains such as control systems, MRs may be derived from design assumptions—e.g., linear time invariance (LTI) yields addition (superposition), scaling, and time-shifting MRs, which can be formally written:
  • Addition/Superposition:

  $$r(t) = r_x(t) + r_y(t) \implies G[r](t) = G[r_x](t) + G[r_y](t)$$ - Scaling:

  $$r(t) = \alpha r_x(t) \implies G[r](t) = \alpha G[r_x](t)$$ - Time-Shifting:

  $$r(t) = r_x(t+\delta) \implies G[r](t) = G[r_x](t+\delta)$$

  (Mandrioli et al., 4 Dec 2024)

• Automated Techniques and Machine Learning: Predicting Metamorphic Relations (PMR) uses features extracted from control-flow graphs of methods to train classifiers that suggest applicable MRs (Duque-Torres et al., 2022). Natural language processing and genetic programming approaches are beginning to automate synthesis from code or documentation (Ayerdi et al., 2023, Duque-Torres et al., 2023).

• Domain-Specific Languages (DSLs): MRs are frequently encoded in DSLs (e.g., SMRL for security, property-oriented DSLs for ML or Web systems) to facilitate composition, automation, and translation into executable test code (Mai et al., 2019, Chaleshtari et al., 2022).

3. Application in Diverse Contexts

MRs have demonstrated practical value across a wide range of real-world systems:

• Machine Learning and Image Classification: For ML classifiers such as SVMs and deep neural networks (ResNet), MRs are derived from mathematical invariances (e.g., permutation of input features, normalization, scaling). A correctly implemented system should yield invariant or predictably altered outputs under these transformations. Mutation analysis on SVM and ResNet classifiers produced a 71% bug detection rate using MRs, illustrating the approach’s efficacy (Dwarakanath et al., 2018).

• Supervised Classifiers: For kNN, MRs include attribute permutation, affine transformations, and consistency under input duplication or removal. However, large-scale experimental studies report that only 14.8% of a broad mutant pool are detected by existing MRs, highlighting scalability and MR selection challenges in practice (Saha et al., 2019).

• Security Testing in Web Systems: MRs encode security properties such as authorization constraints, input validation, and session integrity. In Metamorphic Security Testing frameworks, DSL-expressed system-agnostic MRs are automatically compiled into test oracles, significantly automating security validation (up to 39% of OWASP security activities covered) (Mai et al., 2019, Chaleshtari et al., 2022).

• Smart Contracts on Blockchain: MRs target properties like state transition consistency and aggregation fairness. When applied to Ethereum crowdfunding contracts, MRs tailored to state transitions and donation decomposition could detect up to 89% of seeded mutants in specific categories, demonstrating applicability to immutably deployed systems (Villanueva et al., 17 Jan 2025).

• Fairness in LLMs and Generative Systems: MRs serve to formalize invariance to sensitive attributes through controlled perturbations. Prioritization strategies based on multi-dimensional textual diversity metrics have improved fault detection rates (up to 22% over random orderings) for fairness faults in GPT-4.0 and LLaMA 3.0 (Giramata et al., 9 May 2025).

• Recommender Systems with LLMs: MRs are constructed for both traditional recommendation logic (e.g., rating scale invariance) and LLM language-prompt perturbations, revealing that LLM-based recommendations are sensitive to minor prompt changes, which standard accuracy metrics alone would not capture (Khirbat et al., 18 Nov 2024).

4. Mutation Analysis, Fault Detection, and Test Adequacy

Evaluating the effectiveness of MRs as test oracles relies on mutation analysis (the injection of artificial faults or "mutants" into the SUT):

• Detection Metrics: The percentage of killed mutants is a standard measure. Detection rates can vary significantly with system size, mutation engine, and the spectrum of MRs chosen.

• MR Prioritization: Given variable fault detection power across MRs, automated strategies improve efficiency:

  • Fault-based: Historical fault detection is used to greedily select the most effective MRs.
  • Coverage-based: MRs are prioritized based on code coverage uniqueness.
  • Execution-profile-based: Statement centrality and execution profile dissimilarity are employed; such approaches have increased fault detection effectiveness by up to 31% compared to code coverage and cut detection time by 29% compared to random orderings (Srinivasan et al., 14 Nov 2024).
  • Diversity-based (Textual for LLMs): Aggregated metrics (cosine similarity, lexical and semantic diversity, sentiment, tone) prioritize MRs that are most likely to reveal fairness problems, balancing computational cost and detection performance (Giramata et al., 9 May 2025).
• Test Adequacy: New adequacy criteria for MT measure MR coverage per input (k-MR coverage), guiding the construction of effective test suites. Empirically, larger and more diverse sets of MRs paired with source inputs yield higher fault detection rates, with diminishing returns as k increases (Fu et al., 30 Dec 2024).

5. Automated Generation and Refinement of MRs

Manual MR identification is labor-intensive. Recent work focuses on automating this process:

• Genetic Programming (GP): Techniques such as GenMorph evolve output relation expressions applying fitness functions based on false positive and false negative minimization. Formally, candidate MRs are framed as $R_i(x_1, x_2) \Rightarrow R_o(f(x_1), f(x_2))$. Automation via GP has shown increased mutation scores compared to prior MR generators and enhanced capability when combined with tools like Randoop and Evosuite (Ayerdi et al., 2023).
• PMR (Predicting MRs): Classifiers trained on control-flow graph features predict the applicability of MRs at the unit-testing level, though model transfer across languages (e.g., from Java to Python) is non-trivial and requires retraining on language-specific artifacts (Duque-Torres et al., 2022).
• Association Rule Mining (ARM) in MR Refinement: When MR violations are observed, ARM is employed to differentiate violations due to SUT bugs versus cases where the MR conditions are inapplicable for certain inputs. Key ARM metrics include support, confidence, and lift, guiding regression test suite refinement (Duque-Torres et al., 2023).
• Constraint-augmented and Explainable MRs: Not all MRs are globally valid; constraints capturing input subdomains improve MR applicability, filtering, and fault localization. Visual analytics tools and formal DSLs facilitate understanding and explainability of MR outcomes (Duque-Torres et al., 2023).

6. Impact, Limitations, and Research Directions

Metamorphic relations have established themselves as a primary vehicle for addressing the test oracle problem in domains such as ML, security, smart contracts, Web systems, and LLMs. However, the overall effectiveness of MT is strongly dependent on the quality, appropriateness, and diversity of the chosen MRs, as well as the systematic nature of their pairing with input test cases.

Major findings and open challenges include:

• Scalability and Generalizability: While empirically, MRs can catch the majority of critical faults, studies reveal that existing MRs may not scale well to large or complex mutant pools, or across programming languages, highlighting a need for more algorithmic and domain-specific MR design (Saha et al., 2019, Duque-Torres et al., 2022).
• Test Efficiency: Automated prioritization, constraint-based filtering, and adaptive test suite construction are essential for practical adoption, given the rising volume of potential test cases in modern systems (Srinivasan et al., 14 Nov 2024, Fu et al., 30 Dec 2024).
• Explainability and MR Validity: Tooling to explain MR violations (distinguishing true bugs from violations due to unmet MR preconditions) enhances both fault localization and trust in MT, especially in regression and safety-critical contexts (Duque-Torres et al., 2023, Duque-Torres et al., 2023).
• Integration with AI/ML Tools: The increasing use of LLMs and GPT-4–like models for MR generation suggests an emerging hybrid paradigm where human expertise, automated MR synthesis, and AI-guided evaluation are used together. GPT-4 is shown to reliably produce structurally clear, accurate, and occasionally novel MRs, with evaluation frameworks quantifying MR quality along dimensions like completeness, correctness, clarity, generalizability, novelty, and computational feasibility (Zhang et al., 28 Mar 2025).
• Domain-Specific Extensions: For domains like security or cyber-physical systems, DSLs and design-assumption–based MRs facilitate targeted, highly automated, and explainable testing (Chaleshtari et al., 2022, Mandrioli et al., 4 Dec 2024).

7. Summary Table: MR Effectiveness and Application Domains

Application Domain	Notable MR Strategies	Highlighted Effectiveness
ML Classifiers (SVM/ResNet)	Permutation, scaling, normalization	71% bug detection via MRs (Dwarakanath et al., 2018)
kNN Classifiers	Affine, permutation, duplication	14.8% kill rate for large mutant sets (Saha et al., 2019)
Web System Security	Catalog-based (OWASP/CWE) in DSL	~39% of non-automated OWASP activities (Chaleshtari et al., 2022)
Smart Contracts (Blockchain)	State, donation aggregation, edge handling	Up to 89% mutant detection for core MRs (Villanueva et al., 17 Jan 2025)
LLM Fairness Testing	Attribute-preserving transformations	+22% fault detection over random (Giramata et al., 9 May 2025)
CPS with Control-Theoretic MRs	Addition, scaling, time shift (LTI)	GP search increases MR-falsification rate (Mandrioli et al., 4 Dec 2024)

Metamorphic Relations thus advance the state of software testing by providing a principled, automatable, and adaptable means to assess software correctness in scenarios where explicit oracles are unavailable or insufficient, with ongoing research focusing on automating their generation, selection, and assessment while extending their reach to emerging domains and complex software artifacts.