Automated Concern Extraction from Textual Requirements of Cyber-Physical Systems: A Multi-solution Study

TL;DR

This paper introduces ReqEBench, a comprehensive benchmark for evaluating automated requirements concern extraction methods in cyber-physical systems, highlighting current limitations of AI solutions like GPT-4.

Contribution

It presents ReqEBench, a large, multi-domain benchmark for CPS requirements concern extraction, and provides a comparative analysis of existing automated solutions.

Findings

GPT-4 achieves only 0.24 F1 score in entity concern extraction

Current AI solutions have significant shortcomings in CPS requirements extraction

ReqEBench facilitates future development and evaluation of extraction methods

Abstract

Cyber-physical systems (CPSs) are characterized by a deep integration of the information space and the physical world, which makes the extraction of requirements concerns more challenging. Some automated solutions for requirements concern extraction have been proposed to alleviate the burden on requirements engineers. However, evaluating the effectiveness of these solutions, which relies on fair and comprehensive benchmarks, remains an open question. To address this gap, we propose ReqEBench, a new CPSs requirements concern extraction benchmark, which contains 2,721 requirements from 12 real-world CPSs. ReqEBench offers four advantages. It aligns with real-world CPSs requirements in multiple dimensions, e.g., scale and complexity. It covers comprehensive concerns related to CPSs requirements. It undergoes a rigorous annotation process. It covers multiple application domains of CPSs, e.g., aerospace and healthcare. We conducted a comparative study on three types of automated requirements concern extraction solutions and revealed their performance in real-world CPSs using our ReqEBench. We found that the highest F1 score of GPT-4 is only 0.24 in entity concern extraction. We further analyze failure cases of popular LLM-based solutions, summarize their shortcomings, and provide ideas for improving their capabilities. We believe ReqEBench will facilitate the evaluation and development of automated requirements concern extraction.