针对AI与量子系统的复杂输出，提出反向测试新方法。
Reverse N-Wise Output-Oriented Testing for AI/ML and Quantum Computing Systems
- 从输出等价类出发构建覆盖数组，逆向生成触发特定行为的输入。
- 在ML和量子系统中显著提升故障检测率，尤其对校准与纠错有效。
- 适合需要高可信度验证的AI/量子系统开发与运维团队。
人工智能/机器学习（AI/ML）系统与新兴量子计算软件面临前所未有的测试挑战：输入空间高维连续、输出具有概率性与非确定性，行为正确性仅通过可观测预测结果或测量结果定义，且关键质量维度如可信度、公平性、校准性、鲁棒性、错误综合征模式等，通过语义有意义的输出属性间复杂的多维交互体现，而非确定性映射。本文提出反向n-wise输出测试，一种数学上严谨的范式反转方法：直接在领域特定的输出等价类、模型置信度分桶、决策边界区域、公平性分区、嵌入聚类、排名稳定性带、量子测量结果分布（0主导、1主导、叠加态坍缩）、错误综合征模式（比特翻转、相位翻转、关联错误）上构建覆盖数组，并通过无梯度元启发式优化求解难以处理的黑箱逆映射问题，合成能诱发目标行为特征的输入特征配置或量子电路参数。该框架在两大领域实现协同增益：提供明确的客户导向预测/测量覆盖保障，显著提升对ML校准/边界失效及量子错误综合征的故障检测率，增强测试用例效率，并建立结构化的MLOps/量子验证流水线，支持从不确定性分析与覆盖漂移监控中自动发现分区。
原文摘要 · Abstract (English)
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 mapping problem via gradient-free metaheuristic optimization to synthesize input feature configurations or quantum circuit parameters capable of eliciting targeted behavioral signatures from opaque models. The framework delivers synergistic benefits across both domains: explicit customer-centric prediction/measurement coverage guarantees, substantial improvements in fault detection rates for ML calibration/boundary failures and quantum error syndromes, enhanced test suite efficiency, and structured MLOps/quantum validation pipelines with automated partition discovery from uncertainty analysis and coverage drift monitoring.
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