Dzikri Rahadian Fudholi, Andrea Capiluppi · 2026 · arXiv (Cornell University)
DOI: 10.48550/arxiv.2610.26392 · cited by 0 works (OpenAlex) · open-access PDF
Los roles arquitectónicos describen cómo se distribuyen las responsabilidades dentro de los sistemas de software. Entity--Border--Control (EBC) separa la información del dominio, la interacción externa y la coordinación del flujo de trabajo, pero sus proporciones a través de diversos programas de software siguen siendo inciertas. Este estudio examina la proporcionalidad EBC derivada del clasificador de nivel de archivo en 1.556,985 archivos Java de 732 instancias de proyecto y 78 categorías funcionales. Un clasificador entrenado en etiquetas generadas por un modelo de lenguaje grande asignó un papel representativo por archivo. Las etiquetas se agregaron a los niveles de proyecto y categoría funcional y se analizaron utilizando estadísticas descriptivas y agrupamiento basado en densidad. Los resultados muestran que la Entidad era menos común que el estudio de
Machine translation (Meta M2M100); the English original follows.
Architectural roles describe how responsibilities are distributed within software systems. Entity--Boundary--Control (EBC) separates domain information, external interaction, and workflow coordination, but its proportions across diverse software remain unclear. This study examines file-level classifier-derived EBC proportionality in 1,556,985 Java files from 732 project instances and 78 functional categories. A classifier trained on labels generated by a large language model assigned one representative role per file. The labels were aggregated at project and functional-category levels and analysed using descriptive statistics and density-based clustering. The results show that Entity was less common than Boundary and Control. The dominant cluster covered 48 categories, 472 project instances, and 47.75% of classified files; its mean composition was 13.25% Entity, 42.65% Boundary, and 44.10% Control. The remaining 30 categories form one alternative and 11 smaller specialised patterns. Some selected variations were consistent with category responsibilities, while others received mixed support. Agreement with independent expert labels was limited (31.37% accuracy; macro-F1 0.31; Entity recall 13%). All reported proportions and clusters are therefore patterns in classifier-assigned roles, not verified architectural ground truth. Within this measurement boundary, the study provides a large-scale empirical reference of EBC distributions at the file level, and it documents the limitations of the automated approach.
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Topics: Computer science, Java, Software, Workflow, Data mining, Precision and recall
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