Dzikri Rahadian Fudholi, Andrea Capiluppi · 2026 · arXiv (Cornell University)
DOI: 10.48550/arxiv.2610.26392 · cited by 0 works (OpenAlex) · open-access PDF
文件验证角色描述了如何在软件系统中分配责任。 Entity--Border--Control(EBC)分离了域信息、外部交互和工作流程协调,但其在各种软件上的比例仍然不清楚。本研究研究了文件级分类器衍生的ECB比例性,从732个项目实例和78个功能类别中分类了1556985个Java文件。由大型语言模型生成的标签训练的分类器分配了每个文件的一个代表性角色。标签在项目和功能类别水平上进行了汇总,并使用描述性统计和密度为基础的分类进行分析。结果显示了Entity的研究比Border和Control更少常见。主导性涵盖了48个类别、472个项目实例和4775%的分类文件
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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