Search-Based Risk Feature Discovery in Document Structure Spaces under a Constrained Budget

• URL: http://arxiv.org/abs/2601.21608v1
• Date: Thu, 29 Jan 2026 12:14:18 GMT
• Title: Search-Based Risk Feature Discovery in Document Structure Spaces under a Constrained Budget
• Authors: Saisubramaniam Gopalakrishnan, Harikrishnan P M, Dagnachew Birru,
• Abstract summary: Early-phase system validation under limited budgets mandates uncovering diverse failure mechanisms.<n>We formalize this challenge as a Search-Based Software Testing (SBST) problem.<n>Our methodology operates on a space of document configurations, rendering instances of structural emphrisk features to induce realistic failure conditions.
• Score: 1.2507839249605703
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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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