Authors:
Shuaicai Ren
;
Hiroyuki Nakagawa
and
Tatsuhiro Tsuchiya
Affiliation:
Graduate School of Information Science and Technology, Osaka University, Suita, Japan
Keyword(s):
LLM, Goal Modeling, User Reviews.
Abstract:
User reviews are a valuable resource for developers, as the reviews contain requests for new features and bug reports. By conducting the requirements analysis of user reviews, developers can gain timely insights for the application, which is crucial for continuously enhancing user satisfaction. The goal model is a commonly used model during requirements analysis. Utilizing reviews to generate goal models can assist developers in understanding user requirements comprehensively. However, given the vast number of reviews, manually collecting reviews and creating goal models is a significant challenge. A method for clustering user reviews and automatically generating goal models has been proposed. Nevertheless, the accuracy of the goal models generated by this method is limited. To address these limitations of the existing method and enhance precision of goal model generation, we propose a goal-generation process based on Large Language Models (LLMs). This process does not directly gener
ate goal models from user reviews; instead, it treats goal model generation as a clustering problem, allowing for the visualization of the relationship between reviews and goals. Experiments demonstrate that compared to the existing method, our LLM-based goal model generation process enhance the precision of goal model generation.
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