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LLMs for Uncertainty Identification in Self-Adaptive Robotics

This repository contains supplementary materials as part of the following paper.

๐Ÿ“ Paper Reference

Hassan Sartaj, Jalil Boudjadar, Mirgita Frasheri, Shaukat Ali, and Peter Gorm Larsen.
"Identifying Uncertainty in Self-Adaptive Robotics with Large Language Models."
arXiv preprint arXiv:2504.20684 (2025).
Link to the paper

๐Ÿ“‚ Repository Contents

1. Prompt (LLMsPrompt.pdf) File

  • A role-based prompt designed to identify uncertainties in self-adaptive robotics using large language models (LLMs).
  • Includes:
    • Step-by-step instructions for LLMs.
    • An uncertainty questionnaire covering key uncertainty factors across the robotics software engineering lifecycle.

2. Findings and Analysis (Appendix-Additional-Results.pdf) File

  • Results from additional analyses, including:
    • Cohenโ€™s Kappa (K): Used to assess inter-rater reliability of the results.
    • Non-parametric Confidence Intervals: Calculated using the Bootstrap method for more statistical insights.

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LLM Prompt for Robotic Uncertainty Identification

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