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[2602.14275] Reverse N-Wise Output-Oriented Testing for AI/ML and Quantum Computing Systems
Summary
The paper introduces Reverse N-Wise Output-Oriented Testing, a novel approach for testing AI/ML and quantum computing systems, addressing challenges in behavioral correctness and fault detection.
Why It Matters
As AI/ML and quantum computing technologies evolve, traditional testing methods become inadequate. This research proposes a new testing paradigm that enhances the reliability and trustworthiness of these systems, which is crucial for their adoption in critical applications.
Key Takeaways
- Introduces a new testing framework for AI/ML and quantum systems.
- Focuses on behavioral correctness and fault detection improvements.
- Utilizes gradient-free metaheuristic optimization for input configuration synthesis.
- Enhances test suite efficiency and MLOps validation pipelines.
- Addresses critical quality dimensions like fairness and robustness.
Computer Science > Machine Learning arXiv:2602.14275 (cs) [Submitted on 15 Feb 2026] Title:Reverse N-Wise Output-Oriented Testing for AI/ML and Quantum Computing Systems Authors:Lamine Rihani View a PDF of the paper titled Reverse N-Wise Output-Oriented Testing for AI/ML and Quantum Computing Systems, by Lamine Rihani View PDF Abstract:Artificial intelligence/machine learning (AI/ML) systems and emerging quantum computing software present unprecedented testing challenges characterized by high-dimensional/continuous input spaces, probabilistic/non-deterministic output distributions, behavioral correctness defined exclusively over observable prediction behaviors and measurement outcomes, and critical quality dimensions, trustworthiness, fairness, calibration, robustness, error syndrome patterns, that manifest through complex multi-way interactions among semantically meaningful output properties rather than deterministic input-output mappings. This paper introduces reverse n-wise output testing, a mathematically principled paradigm inversion that constructs covering arrays directly over domain-specific output equivalence classes, ML confidence calibration buckets, decision boundary regions, fairness partitions, embedding clusters, ranking stability bands, quantum measurement outcome distributions (0-dominant, 1-dominant, superposition collapse), error syndrome patterns (bit-flip, phase-flip, correlated errors), then solves the computationally challenging black-box inverse map...
