DyTSSAM: A Dynamic Dependency Analysis Model Based on DAST

Program dependence analysis plays a fundamental role in program comprehension, software maintenance, and defect detection. However, existing static approaches—such as those based on Program Dependence Graphs or Abstract Syntax Trees—struggle to model fine-grained syntactic changes and fail to capture how dependencies evolve as code changes over time. To address these limitations, this study proposes DyTSSAM, a dynamic dependency analysis model built upon the Dynamic Abstract Syntax Tree (DAST). DyTSSAM decomposes DAST into temporally ordered change subtrees to capture the minimal syntactic units of code evolution, and incorporates local–global dependency analysis to enrich node representations with heterogeneous dependency information. The model further integrates a dynamic structural-syntax layer and a temporal-semantic layer, which jointly learn dynamic syntactic structures and temporal dependency patterns through a dynamic graph neural network. Experiments conducted on real-world datasets compare DyTSSAM with seven state-of-the-art dynamic graph neural networks. Results demonstrate that DyTSSAM achieves significantly higher AUC and AP scores, improves fine-grained modeling of node- and subtree-level dependencies, and exhibits greater sensitivity in capturing dependency evolution throughout code changes. To support reproducibility and enable future research, the complete datasets, preprocessing code, and model implementation are publicly available on GitHub. These findings suggest that DyTSSAM provides an effective and scalable framework for dynamic program dependence analysis.