Leveraging an LLM to Generate Goal Models

Abstract

Goal-Oriented Requirements Analysis (GORA) is a technique, in which 1) customer requirements are captured as goals, 2) then they are iteratively and incrementally refined and 3) finally a goal model is constructed in order to derive system and software requirements. However, the lack of comprehensive guidelines and methodologies for goal refinement makes it challenging for beginners to construct appropriate models, often resulting in low-quality goal models. This paper focuses on iStar, one of the GORA techniques and proposes a novel approach using GPT-4o to automatically generate iStar models of higher quality from simple problem descriptions written in natural language. Our technique contains two processes. In the first process, we provide GPT-4o with a step-by-step generation procedure and syntax rules for iStar models in order to minimize syntactic violations of a generated iStar model. By using Self-Instruct, without providing a large amount of training data for GPT-4o, our second process detects and corrects semantically low-quality model elements such as ambiguity, omissions and lack of consistency within an iStar model. Our experimental results demonstrated that 76.7% of the iStar models generated in the first process did not include any syntax errors and that in the second process a detection rate of 78.6% and correction rate of 100% were achieved.