Graph Neural Networks for Predicting Software Architectural Drift

Abstract

Software architectural drift arises when a software system’s implementation gradually diverges from its intended architectural design, leading to reduced maintainability, increased technical debt, and higher evolution costs. Traditional drift detection methods rely heavily on manual analysis, rule-based constraints, static metrics, or architectural conformance checking, all of which struggle to capture complex and evolving structural dependencies. This paper proposes a Graph Neural Network (GNN-based) predictive framework for modeling software architecture as heterogeneous dependency graphs and identifying potential drift before it manifests in code-level violations. The approach integrates structural features, semantic code embeddings, version-history evolution patterns, and architectural constraints into a unified graph learning model. Experimental evaluation on open-source and industrial projects demonstrates that the proposed GNN model outperforms conventional static analysis and machine learning baselines in predicting architectural deviations, detecting anomalous dependency formations, and flagging early indicators of structural degradation. The study contributes a scalable, learning-based methodology for proactive architectural governance and long-term software quality preservation.