JOURNAL ARTICLE
SHAMROQ: A Software Engineering Methodology to Extract Deontic Expressions from the Code of Federal Regulations — A Single-Case, Embedded Case Study.
Published In: International Journal of Software Engineering & Knowledge Engineering, 2023, v. 33, n. 11/12. P. 1787 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Cook, Patrick D.; Mengel, Susan A.; Parameswaran, Siva 3 of 3
Abstract
This research provides a comprehensive analysis of deontic expressions within the Code of Federal Regulations (CFR) Title 48, Federal Acquisition Regulations System, specifically focusing on obligations, permissions, prohibitions, and dispensations. Utilizing SHAMROQ, a systematic and rigorous methodology, the authors extract, classify, and analyze these expressions, quantify their prevalence, and identify common linguistic patterns within the legal text. The results show that obligations (71.3%) form most deontic expressions in CFR 48, indicating the heavily prescriptive nature of the document. Permissions also form a significant part (21.9%), suggesting the liberties and allowances are embedded within the regulatory framework. In contrast, prohibitions (5.4%) and dispensations (1.4%) are less frequent, indicating that the document leans more towards defining what is required or allowed rather than what is explicitly forbidden or exempted. This research also highlights the challenges encountered during the extraction process, providing insights into the complexities of parsing legal texts and the intricacies of deontic language. These challenges range from the technical difficulties of parsing a complex hierarchical document to the conceptual challenges of defining precise rulesets for regulations and provisions. In summary, the results deepen the understanding of regulatory compliance in software engineering and contribute to the development of more effective and efficient automated extraction tools. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:International Journal of Software Engineering & Knowledge Engineering. 2023/11, Vol. 33, Issue 11/12, p1787
- Document Type:Article
- Subject Area:Computer Science
- Publication Date:2023
- ISSN:0218-1940
- DOI:10.1142/S021819402341005X
- Accession Number:174823472
- Copyright Statement:Copyright of International Journal of Software Engineering & Knowledge Engineering is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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