Error Propagation Topologies in AI-Assisted Construction Pipelines

Abstract

AI-assisted software construction pipelines have introduced significant improvements in automation and development efficiency, but they also give rise to complex error propagation behaviors across multiple stages. Existing approaches primarily focus on detecting isolated errors, often overlooking how errors evolve, transform, and intensify as they move through interconnected pipeline stages. This study investigates error propagation as a topological flow problem, categorizing errors into syntactic, semantic, logical, dependency, and integration types, and analyzing their propagation intensity across code generation, compilation, testing, integration, and deployment stages. The results reveal that error propagation is highly stage-dependent, with testing and integration acting as major amplification points, while transformation processes convert errors into more complex forms, making detection and tracing challenging. A heatmap-based analysis further demonstrates the non-uniform distribution of propagation intensity, highlighting critical zones where errors escalate. The findings emphasize the need for stage-aware and transformationaware mitigation strategies to ensure reliability and robustness in AI-driven software pipelines.