Dzikri Rahadian Fudholi, Andrea Capiluppi · 2026 · arXiv (Cornell University)
DOI: 10.48550/arxiv.2610.26392 · cited by 0 works (OpenAlex) · open-access PDF
Архитектурные роли описывают, как ответственность распределяется в программных системах. Entity--Border--Control (EBC) отделяет информацию о доменах, внешнее взаимодействие и координацию рабочего процесса, но его пропорции по различным программным обеспечению остаются неясными. Это исследование исследует пропорциональность EBC на уровне файлов-классификатора в 1 556 985 файлах Java из 732 экземпляров проекта и 78 функциональных категорий. Классификатор, обученный на кластерах, генерируемых большой языковой моделью, присвоил одну представительную роль на файл. Этикетки были агрегированы на уровне проекта и функциональной категории и проанализированы с использованием описательной статистики и кластеризации на основе плотности. Результаты показывают, что
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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