Software development projects frequently encounter cost and time efficiency challenges, rendering them susceptible to failure. An imprecise and incomplete process of eliciting user software requirements can further escalate software development’s time, cost, and effort. Consequently, the reuse of user stories based on application descriptions for software requirement elicitation is proposed to mitigate these issues and enhance cost efficiency. This research aims to evaluate the reuse of user stories within the software elicitation process by extracting software features from initial application descriptions. The goal is to determine whether this approach can effectively reduce the time and cost associated with software development. This study employs Natural Language Processing (NLP) techniques to identify Part-Of-Speech (POS) tag patterns to extract software features from application descriptions. A similarity measurement is conducted between user stories and the extracted software features. User stories are then filtered based on a similarity threshold to ensure high relevance to the extracted software features. The proposed method was tested using three distinct application descriptions alongside 22 user story datasets provided to nine respondents. The analysis indicates that the reuse of user stories based on feature extraction from application descriptions, determined through Cosine similarity with an 80% threshold, significantly enhances software development efficiency in terms of time and cost. The method demonstrated an average precision of 84%, recall of 93%, and F1-score of 86% across the three test projects, confirming its alignment with system design requirements. The findings of this research validate the effectiveness of the Cosine similarity method in identifying reusable user stories from application descriptions. This approach is a viable solution for reusing user stories in the software requirement elicitation process, thereby improving efficiency in software development projects.

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