Refactoring goal-oriented models: a linguistic improvement using large language models.
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| Title: | Refactoring goal-oriented models: a linguistic improvement using large language models. |
|---|---|
| Authors: | Alturayeif, Nouf1,2 (AUTHOR) g201901790@kfupm.edu.sa, Hassine, Jameleddine1,3 (AUTHOR) jhassine@kfupm.edu.sa |
| Source: | Software & Systems Modeling. Feb2026, Vol. 25 Issue 1, p51-79. 29p. |
| Subjects: | Requirements engineering, Software refactoring, Natural language processing, Language models, Goal (Psychology) |
| Abstract: | 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. [ABSTRACT FROM AUTHOR] |
| Copyright of Software & Systems Modeling is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 192012041 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Refactoring goal-oriented models: a linguistic improvement using large language models. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Alturayeif%2C+Nouf%22">Alturayeif, Nouf</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> g201901790@kfupm.edu.sa</i><br /><searchLink fieldCode="AR" term="%22Hassine%2C+Jameleddine%22">Hassine, Jameleddine</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<i> jhassine@kfupm.edu.sa</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Software+%26+Systems+Modeling%22">Software & Systems Modeling</searchLink>. Feb2026, Vol. 25 Issue 1, p51-79. 29p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Requirements+engineering%22">Requirements engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Software+refactoring%22">Software refactoring</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+language+processing%22">Natural language processing</searchLink><br /><searchLink fieldCode="DE" term="%22Language+models%22">Language models</searchLink><br /><searchLink fieldCode="DE" term="%22Goal+%28Psychology%29%22">Goal (Psychology)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Software & Systems Modeling is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10270-024-01254-1 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 29 StartPage: 51 Subjects: – SubjectFull: Requirements engineering Type: general – SubjectFull: Software refactoring Type: general – SubjectFull: Natural language processing Type: general – SubjectFull: Language models Type: general – SubjectFull: Goal (Psychology) Type: general Titles: – TitleFull: Refactoring goal-oriented models: a linguistic improvement using large language models. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Alturayeif, Nouf – PersonEntity: Name: NameFull: Hassine, Jameleddine IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 16191366 Numbering: – Type: volume Value: 25 – Type: issue Value: 1 Titles: – TitleFull: Software & Systems Modeling Type: main |
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