Failure Mode Taxonomy for AI-Assisted Software Assembly in Industries

Abstract

Artificial intelligence–assisted software assembly is increasingly used in regulated fields such as healthcare and clinical analytics, where reliability, traceability, and compliance are essential. However, AI pipelines remain sensitive to data structure variability, semantic mismatch, and integration inconsistencies. This study proposes a structured taxonomy of failure modes in AI-assisted software assembly pipelines, focusing on structural inconsistencies, semantic misalignments, execution anomalies, and integration breakdowns. A formalized pipeline model with failure propagation functions is developed to evaluate the impact of different failure types across assembly stages. Results show nonlinear amplification of failure effects, with major instability in feature engineering, model integration, and validation layers, especially under cumulative and evolving perturbations. The findings show that conventional mitigation strategies are insufficient for regulated environments. The proposed framework supports schema-aware, adaptive, and compliance-driven system design to improve robustness and reliability in healthcare diagnostics, microbiological surveillance, and telemedicine platforms.