Engineering Safety Requirements for Autonomous Driving with Large Language Models
Paper i proceeding, 2024

Changes and updates in the requirement artifacts, which can be frequent in the automotive domain, are a challenge for SafetyOps. Large Language Models (LLMs), with their impressive natural language understanding and generating capabilities, can play a key role in automatically refining and decomposing requirements after each update. In this study, we propose a prototype of a pipeline of prompts and LLMs that receives an item definition and outputs solutions in the form of safety requirements. This pipeline also performs a review of the requirement dataset and identifies redundant or contradictory requirements. We first identified the necessary characteristics for performing HARA and then defined tests to assess an LLM's capability in meeting these criteria. We used design science with multiple iterations and let experts from different companies evaluate each cycle quantitatively and qualitatively. Finally, the prototype was implemented at a case company and the responsible team evaluated its efficiency.

ChatGPT

LLM

Large Language Model

DevOps

Autonomous Vehicles

Prompt Engineering

Hazard Analysis Risk Assessment

Safety

Requirement Engineering

Författare

Ali Nouri

Software Engineering 1

Beatriz Cabrero-Daniel

Software Engineering 2

Fredrik Torner

Hakan Sivencrona

Christian Berger

Software Engineering 2

2024 IEEE 32nd International Requirements Engineering Conference (RE)

2332-6441 (ISSN)

2024 IEEE 32nd International Requirements Engineering Conference (RE)
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Ämneskategorier

Programvaruteknik

DOI

10.1109/RE59067.2024.00029

Mer information

Skapat

2024-09-03