Feature-Driven Generation of Software Networks with Structural and Semantic Alignment

Software call graphs encode both the structural organization and interaction semantics of software systems. Existing network generators, however, usually emphasize local degree statistics or coarse topological similarity, and often cannot preserve community structure, global degree-pairing patterns, and degree–weight dependence simultaneously. This paper proposes a feature-driven framework for generating directed dual-weight software networks from function-call graphs, where code size is modeled as node weight and static call frequency is modeled as edge weight. The framework combines correlation-aware node-metric generation, community-preferential connectivity, assortativity-guided edge rewiring, and semantics-aware edge-weight assignment to improve the joint preservation of topological and weighted properties. Experimental results on four real software-network datasets, including Large-CG, the largest weakly connected component of an extracted Visual Studio Code function-call graph, demonstrate that the generated networks effectively reproduce key structural and weighted characteristics of the originals. In addition, ablation studies confirm the role of individual components, and scale-control experiments show stable behavior across different network sizes. Wall-clock runtime experiments under a unified CPU setting further show that the framework remains computationally feasible across all four target scales. The proposed method provides a reproducible and controllable solution for software-network generation and offers useful support for downstream software analysis tasks.