在有限预算下，用多种搜索策略发现文档结构中的风险特征。
Search-Based Risk Feature Discovery in Document Structure Spaces under a Constrained Budget
- 通过组合式文档配置搜索风险特征，模拟真实失败场景。
- 不同搜索策略发现的故障类型互不重叠，无单一方法最优。
- 多策略组合能更全面发现风险，适合工业级文档系统验证。
企业级智能文档处理（IDP）系统支撑金融、保险、医疗等高风险流程。在预算受限的早期验证阶段，需发现多样化的故障机制，而非仅定位最差文档。本文将此问题形式化为基于搜索的软件测试（SBST），目标是在固定评估预算内最大化发现的不同故障类型数量。方法在文档配置的组合空间中生成具有结构性风险特征的实例，以诱发真实故障。我们在相同预算约束下对比了涵盖进化、群体智能、质量-多样性、学习型及量子计算在内的多种搜索策略。通过配置级排他性、胜率与跨时间重叠分析，发现各求解器持续揭示其他方法未覆盖的故障模式，且无任何策略在所有预算下表现绝对领先。尽管所有策略联合可覆盖已观测故障空间，但依赖单一方法会系统性延迟重要风险的发现。结果表明求解器间存在内在互补性，支持采用组合策略实现稳健的工业级IDP验证。
原文摘要 · Abstract (English)
Enterprise-grade Intelligent Document Processing (IDP) systems support high-stakes workflows across finance, insurance, and healthcare. Early-phase system validation under limited budgets mandates uncovering diverse failure mechanisms, rather than identifying a single worst-case document. We formalize this challenge as a Search-Based Software Testing (SBST) problem, aiming to identify complex interactions between document variables, with the objective to maximize the number of distinct failure types discovered within a fixed evaluation budget. Our methodology operates on a combinatorial space of document configurations, rendering instances of structural \emph{risk features} to induce realistic failure conditions. We benchmark a diverse portfolio of search strategies spanning evolutionary, swarm-based, quality-diversity, learning-based, and quantum under identical budget constraints. Through configuration-level exclusivity, win-rate, and cross-temporal overlap analyses, we show that different solvers consistently uncover failure modes that remain undiscovered by specific alternatives at comparable budgets. Crucially, cross-temporal analysis reveals persistent solver-specific discoveries across all evaluated budgets, with no single strategy exhibiting absolute dominance. While the union of all solvers eventually recovers the observed failure space, reliance on any individual method systematically delays the discovery of important risks. These results demonstrate intrinsic solver complementarity and motivate portfolio-based SBST strategies for robust industrial IDP validation.
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