Refactoring goal-oriented models: a linguistic improvement using large language models

Goal-oriented requirements engineering (GORE) facilitates effective communication and collaboration between stakeholders. Using goal models, GORE provides a structured approach to elicit, analyze, and manage requirements from the perspective of stakeholders’ goals and intentions. However, goal models are prone to several poor practices, called bad smells, which can obstruct effective communication between stakeholders. As a result, there might be misinterpretations and inconsistencies in the requirements. Goal models are particularly prone to linguistic bad smells, encompassing unclear or ambiguous goal statements, conflicting or contradictory requirements, and occurrences of misspellings. It is therefore imperative that linguistic bad smells are identified and addressed in goal models to ensure their quality and accuracy. In this paper, we build upon our previous research by enhancing the catalog of 17 goal-oriented requirements language (GRL) linguistic bad smells. We refine the detection techniques using a combination of NLP-based and LLM-based techniques. These enhancements significantly improved the tool’s detection capabilities compared to our previous work. Furthermore, we offer automated refactoring solutions for 9 of these bad smells through GPT prompts. The remaining four identified bad smells are left to the user’s discretion for refactoring, due to their subjective nature. The detection and refactoring processes are implemented in a tool, tailored to the Textual GRL (TGRL) language. We evaluated the bad smells refactoring approach and tool by administering a questionnaire to 13 participants, who assessed the correctness of the refactoring of 71 linguistic bad smells found in four (4) TGRL models. Participants perceived the refactored sentences as highly correct across the different types of linguistic bad smells.