Combining Prompts with Examples to Enhance LLM-Based Requirement Elicitation

In application marketplace platforms like the Google Play Store, reviews left by users on applications play a vital role for developers. By analyzing user reviews, developers identify potential requirements. The goal model is a commonly used model in requirements analysis. Utilizing reviews to generate goal models can help developers comprehensively understand user requirements. However, manually analyzing a large volume of reviews is a time-consuming and labor-intensive task. To address this problem, an automatic method for clustering user reviews and identifying goal models has been proposed. Nevertheless, the goal model generated by this method has poor accuracy, and the goals generated are difficult for developers to understand. To more comprehensively extract requirements from user reviews, we propose a goal model generation method based on large language models (LLMs). The proposed method consists of two parts: first, a Latent Dirichlet Allocation (LDA) model divides user reviews into different topics; second, we use specific prompts and examples to extract requirements and generate goal models. Experiments show that our LLM-based goal model generation method improves the accuracy of goal model generation and identifies more requirements compared to the existing method.