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ISOprep: an LLM-driven pipeline for semantics-preserving processing of neutralized requirements according to ISO 29148

Schiller, Kata Amanda; Seibel, Arthur

Type:
Papers
DS 145: Proceedings of the DESIGN2026 19th International Design Conference
Year:
2026
Editor:
Štorga, M.; Škec, S.; Martinec, T.; Marjanović, D.; Pavković, N.
Author:
Schiller, Kata Amanda; Seibel, Arthur
Series:
DESIGN
Institution:
Leuphana University Lüneburg, Germany
Page(s):
807-816
DOI number:
10.1017/pds.2026.10439
ISSN:
2732-527X (Online)
Abstract:
AI in Requirements Engineering (RE) relies on industrial data, yet safety and privacy risks limit its use. While the GDPR distinguishes only between anonymization and pseudonymization, we use neutralization as a semantics-preserving technique. In AI-supported RE, data heterogeneity and cross-domain variability impede model training. We propose guidelines for semantics-preserving preprocessing for RE datasets based on ISO 29148 criteria, showing that neutralization does not compromise semantics. The approach enables industry–academia collaboration through AI-assisted RE in product development.
Keywords:
requirements management, artificial intelligence (AI), neutralisation, ISO 29148, data-driven design
DESIGN2026 - The 19th International Design Conference
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