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

Saisubramaniam Gopalakrishnan, Harikrishnan P M, Dagnachew Birru

TL;DR

The study reframes IDP validation as budgeted risk-feature discovery over structured document configurations, using SBST to uncover diverse failure mechanisms under a fixed evaluation budget. It introduces a system-level risk oracle and a synthetic document generator to render test cases with ground-truth annotations, and defines risk signatures and exclusivity metrics to analyze solver complementarity. Across single- and multi-page document setups, a broad portfolio of search strategies (including Bayesian, evolutionary, RL, and quantum-inspired methods) reveals that no single method dominates; instead, solver complementarity enables richer risk discovery and better predictive modeling of future risks. The findings advocate for portfolio-based SBST in industrial IDP validation, showing that combining structure-aware exploration with diversity-focused solvers improves robustness and enables proactive validation and test prioritization, with QAOA-Corr providing distinct exploration advantages. Overall, risk landscapes in IDP are learnable and structured, suggesting practical pathways to harden deployments through diversified, budget-aware testing and predictive risk management.

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.

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

TL;DR

The study reframes IDP validation as budgeted risk-feature discovery over structured document configurations, using SBST to uncover diverse failure mechanisms under a fixed evaluation budget. It introduces a system-level risk oracle and a synthetic document generator to render test cases with ground-truth annotations, and defines risk signatures and exclusivity metrics to analyze solver complementarity. Across single- and multi-page document setups, a broad portfolio of search strategies (including Bayesian, evolutionary, RL, and quantum-inspired methods) reveals that no single method dominates; instead, solver complementarity enables richer risk discovery and better predictive modeling of future risks. The findings advocate for portfolio-based SBST in industrial IDP validation, showing that combining structure-aware exploration with diversity-focused solvers improves robustness and enables proactive validation and test prioritization, with QAOA-Corr providing distinct exploration advantages. Overall, risk landscapes in IDP are learnable and structured, suggesting practical pathways to harden deployments through diversified, budget-aware testing and predictive risk management.

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.
Paper Structure (51 sections, 6 equations, 14 figures, 4 tables, 1 algorithm)

This paper contains 51 sections, 6 equations, 14 figures, 4 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Risk Feature Discovery as Search-Based Software Testing:
  3. A Budget-Matched, System-Level Benchmark for Risk Discovery:
  4. Structural and Cross-Temporal Evidence of Solver Complementarity:
  5. Risk Mode Characterization and Predictive Modeling:
  6. Related Work
  7. Software Testing for Document Processing
  8. OCR and Textual Key-Value Extraction:
  9. Document Layout and Structural Verification:
  10. Tabular Structure and Relational Consistency:
  11. Optimization Primitives for Exploration
  12. Evolutionary, Swarm, and Quality–Diversity Methods:
  13. Bayesian Optimization:
  14. Reinforcement Learning:
  15. Stochastic Local Search:
  16. ...and 36 more sections

Figures (14)

  • Figure 1: Schematic of the Risk Feature Discovery Pipeline
  • Figure 2: Risk-feature level exclusivity over budget for single-page (24D). Each curve reports the fraction exclusively discovered by a method relative to all others across same budget. The dotted red line represents the top 10% mean risk over all other methods, while the dashed green line represents that method's top 10% mean risk.
  • Figure 3: Cross-temporal risk features overlap between two methods. Each facet fixes one method at max budget, while the horizontal axis tracks the growth of discoveries made by the other method. Stacked Blue / Orange areas indicate features exclusive to each method, while Gray indicates those discovered in common.
  • Figure 4: UMap visualization of exclusive discoveries per method. Points represent "Crown Jewel" samples—unique discoveries within the top 10% of risk. The plot highlights the unique contributions of each solver, with diversity-oriented methods (e.g., GA-Explore: 44, MAP-Elites: 42) and interaction-aware explorers (QAOA-Corr: 28) identifying distinct failure modes missed by local search baselines (SA: 19, TPE: 21).
  • Figure 5: Configuration-level exclusivity over budget for all methods in single-page (24D). Each curve reports the fraction of configurations exclusively discovered by a method relative to all others across same budget. The dotted red line represents the top 10% mean risk over all other methods, while the dashed green line represents its top 10% mean risk per budget.
  • ...and 9 more figures