Generative Goal Modeling
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866915472781344768 |
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| author | Sharfuddin, Ateeq Breaux, Travis |
| author_facet | Sharfuddin, Ateeq Breaux, Travis |
| contents | In software engineering, requirements may be acquired from stakeholders through elicitation methods, such as interviews, observational studies, and focus groups. When supporting acquisition from interviews, business analysts must review transcripts to identify and document requirements. Goal modeling is a popular technique for representing early stakeholder requirements as it lends itself to various analyses, including refinement to map high-level goals into software operations, and conflict and obstacle analysis. In this paper, we describe an approach to use textual entailment to reliably extract goals from interview transcripts and to construct goal models. The approach has been evaluated on 15 interview transcripts across 29 application domains. The findings show that GPT-4o can reliably extract goals from interview transcripts, matching 62.0% of goals acquired by humans from the same transcripts, and that GPT-4o can trace goals to originating text in the transcript with 98.7% accuracy. In addition, when evaluated by human annotators, GPT-4o generates goal model refinement relationships among extracted goals with 72.2% accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_01048 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Generative Goal Modeling Sharfuddin, Ateeq Breaux, Travis Software Engineering D.2.1 In software engineering, requirements may be acquired from stakeholders through elicitation methods, such as interviews, observational studies, and focus groups. When supporting acquisition from interviews, business analysts must review transcripts to identify and document requirements. Goal modeling is a popular technique for representing early stakeholder requirements as it lends itself to various analyses, including refinement to map high-level goals into software operations, and conflict and obstacle analysis. In this paper, we describe an approach to use textual entailment to reliably extract goals from interview transcripts and to construct goal models. The approach has been evaluated on 15 interview transcripts across 29 application domains. The findings show that GPT-4o can reliably extract goals from interview transcripts, matching 62.0% of goals acquired by humans from the same transcripts, and that GPT-4o can trace goals to originating text in the transcript with 98.7% accuracy. In addition, when evaluated by human annotators, GPT-4o generates goal model refinement relationships among extracted goals with 72.2% accuracy. |
| title | Generative Goal Modeling |
| topic | Software Engineering D.2.1 |
| url | https://arxiv.org/abs/2509.01048 |