Dzikri Rahadian Fudholi, Andrea Capiluppi · 2026 · arXiv (Cornell University)
DOI: 10.48550/arxiv.2610.26392 · cited by 0 works (OpenAlex) · open-access PDF
Entity--Border--Control (EBC) memisahkan informasi domain, interaksi eksternal, dan koordinasi alur kerja, tetapi proporsinya di berbagai perangkat lunak tetap tidak jelas. Studi ini mengeksplorasi proporsionalitas EBC yang berasal dari klasifikator tingkat file dalam 1.556,985 file Java dari 732 instansi proyek dan 78 kategori fungsional. Sebuah klasifikator yang dilatih pada kluster yang dihasilkan oleh model bahasa besar mengalokasikan satu peran representatif per file. Label ini dikumpulkan pada tingkat proyek dan kategori fungsional dan dianalisis menggunakan statistik deskriptif dan cluster berbasis kepadatan. Hasilnya menunjukkan bahwa Entity kurang umum dari Boundary dan Control. Sebuah klasifikator yang didominasi mencakup 48 kategori, 472 instansi proyek, dan 47.75% dari file yang diklasifikasikan; rasio komposis
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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