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LLM prompts extract software goals with 61% accuracy, aiding manual efforts

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a method using a chain of LLMs with engineered prompts to automate the extraction of functional goals from software documentation. This approach involves actor identification and high/low-level goal extraction, incorporating a generation-critic mechanism as a feedback loop between two LLMs. While the pipeline achieved 61% accuracy in low-level goal identification, it is best suited to accelerate manual extraction rather than fully replace it. Future work aims to improve accuracy by integrating Retrieval-Augmented Generation (RAG) and Chain-of-Thought (CoT) prompting. AI

IMPACT Provides a tool to accelerate manual goal extraction in software engineering, with potential for future improvements via RAG and CoT.

RANK_REASON Academic paper detailing a new method for LLM-based goal extraction in software engineering.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

LLM prompts extract software goals with 61% accuracy, aiding manual efforts

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Academic paper detailing a new method for LLM-based goal extraction in software engineering.
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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Anna Arnaudo, Riccardo Coppola, Maurizio Morisio, Flavio Giobergia, Andrea Bioddo, Angelo Bongiorno, Luca Dadone ·

    Evaluating LLM-Based Goal Extraction in Requirements Engineering: Prompting Strategies and Their Limitations

    arXiv:2604.22207v1 Announce Type: cross Abstract: 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 f…

  2. arXiv cs.CL TIER_1 English(EN) · Luca Dadone ·

    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 Engin…

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