<![CDATA[Developing safety-critical systems (SCS) involves a systematic method for assuring and providing safety and dependability. Conventional approaches rely on expert intervention, which can introduce bias, cause delays, and promote inconsistency. This work proposes a model that enhances efficiency and accuracy by extracting safety functions from requirements specifications. The model is made up of three main steps: (1) preprocessing, which involves getting rid of stop words; (2) string selection and matching using a database of safety properties variables based on literature and expert knowledge; and (3) putting safety and non-safety functions into a structured safety function log. The model was trained and tested with the CGPA insulin pump and got a 94% F1 measure score, which means it was 91% accurate, 96% accurate, 92% precise, and 96% recall. This shows that it is good at making things clearer and less biased when finding functions for safety against failures, malfunctions, operational hazards, and inconsistencies in safety-critical specifications. All these enhancements contribute towards Sustainable Development Goal (SDG) 11: Sustainable Cities and Communities, aiming to develop safer, resilient, and sustainable infrastructure in safety-critical regions.]]>
