World Requirement Model: Learning Requirement-Change Consequences from Typed Artifact Graphs

2026-10-03

AI Summary

This study addresses the challenge of accurately predicting how requirement changes propagate to interdependent elements such as stakeholders, constraints, and tests. To this end, it proposes an artifact-addressable world model that constructs typed artifact graphs to encode engineering context. By integrating relation-aware attention with type propagation mechanisms and coupling them with world-decision representation learning for dynamic evolution, the approach achieves context-aware consequence scoring through a shared readout layer. Evaluated on synthetic cases, the method attains an impact prediction Mean Average Precision (MAP) of 0.724, representing a 16.2% improvement over baselines, while the lowest-quartile Average Precision increases by 32.4%. Demonstrating significant superiority across multiple metrics compared to competing systems, this work establishes a comprehensive evaluation paradigm for requirement world prediction.

Abstract

Requirement changes can affect connected stakeholders, constraints, components, and tests. We present World Requirement Model (WRM), which encodes this engineering context as a typed artifact graph and predicts consequences at shared artifact identifiers. Relation-aware attention and typed propagation contextualize nodes; world and decision representations support learned dynamics. Shared readouts score impact, conflict, violation, and defect risk; auxiliary objectives supervise successor adjacency and latent prediction. On 28 scored cases from 282 synthetic cases in six domains, WRM obtains impact mean average precision (MAP) of 0.724 versus 0.623 for a hashed-text multilayer perceptron (MLP), a 16.2\% relative gain and paired difference of 0.101 (conditional 95\% interval [0.044,0.159]). Lowest-quarter mean AP improves by 32.4\%, and equal-domain MAP by 13.3\%. Four 47-case comparisons on an expanded corpus show MAP gains of 15.6--29.1\% and higher means on all five reported metrics. The recorded advantage thus extends across score summaries and annotation/training settings. Checkpoints were selected on scored cases, and backbones are unmatched, so these results characterize selected systems. Our analysis establishes candidate-coverage bounds and shows that the current linear impact head cannot rerank a fixed world's artifacts across decisions. WRM contributes an artifact-addressed world-model formulation, comparative evidence for contextual consequence scoring, and explicit conditions for evaluating requirement-world prediction.

Problem

• requirement change
• consequence prediction
• impact analysis
• artifact graph
• risk assessment

Innovation

• World Requirement Model
• Typed Artifact Graph
• Relation-aware Attention
• Consequence Prediction
• Requirement Engineering