Intelligent Detection of Design Patterns in Existing Source Code

Detecting design patterns in source code is essential for understanding and maintaining software architectures, yet remains difficult due to variability in real-world implementations. This paper introduces a neuro-symbolic framework for design pattern detection that combines interpretable structural analysis with learned semantic representations. The approach integrates two complementary signals, i.e., topological conformance via abstract syntax tree analysis under a four-pillar heuristic, and semantic similarity to canonical pattern prototypes in a discriminative latent space. This latent space is obtained by fine-tuning CodeBERT with supervised contrastive learning, yielding cohesive and separable pattern clusters. To mitigate data scarcity and lexical bias, we generate and validate a synthetic dataset using large language models and apply identifier anonymization at the abstract syntax tree level. Evaluation on an unseen synthetic test set and on a manually validated Gold Standard benchmark demonstrates improved robustness over purely structural methods, with strong performance across standard metrics. Overall, combining structural constraints with optimised semantic representations offers a robust and scalable solution for design pattern detection in software architecture analysis.