Evaluating LLM-Based Goal Extraction in Requirements Engineering: Prompting Strategies and Their Limitations
Due to the textual and repetitive nature of many Requirements Engineering (RE) artefacts, Large Language Models (LLMs) have proven useful to automate their generation and processing. In this paper, we discuss a possible approach for automating the Goal-Oriented Requirements Engineering (GORE) process by extracting functional goals from software documentation through three phases: actor identification, high and low-level goal extraction. To implement these functionalities, we propose a chain of LLMs fed with engineered prompts. We experimented with different variants of in-context learning and measured the similarities between input data and in-context examples to better investigate their ...
| Authors | Anna Arnaudo, Riccardo Coppola, Maurizio Morisio et al. |
|---|---|
| Primary category | cs.SE |
| Published | 24 April 2026 |
| Last updated at source | 4 August 2026 |
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