Large Language Model Based Approach for Automatic Software Requirements Structuring and Change Tracking

Unstructured software requirements text faces challenges of low processing efficiency and poor consistency and needs to be transformed into a structured form. This paper presents an automation framework based on a large language model that integrates text preprocessing, fine prompt engineering, and requirements association network construction techniques. It precisely parses the semantics of requirements in order to automatically convert them into structured form and analyzes the impact of changes, providing strong support for requirements engineering. Experimental results show that the method performs exceptionally well in terms of requirements structuring accuracy, with an F1 score of 90.8%, and in terms of change tracking efficiency, with an average time consumption of only 3.5 seconds, both significantly better than traditional methods. This study enhanced the automation and intelligence of requirements engineering.