Utilizing Process Models in the Requirements Engineering Process Through Model2Text Transformation

Abstract

With the advent of large language models (LLMs), requirements engineers have gained a powerful natural language processing tool to analyze, query, and validate a wide variety of textual artifacts, thus potentially supporting the whole re-quirements engineering process from requirements elicitation to management. However, the input for the requirements engineering process often encompasses a variety of potential information sources in various formats, especially graphical models such as process models. Hence, this work aims to contribute to the state of the art by assessing the feasibility of utilizing graphical process models and their textual representations in the requirements engineering process. In particular, we focus on the extraction of textual process descriptions from process models as i) input for the requirements engineering process and ii) documentation as the result of process-oriented requirements engineering. To this end, we explore, quantify, and compare traditional deterministic and LLM-based extraction methods where the latter includes GPT3, GPT3.5, GPT4, and LLAMA. The evaluation assesses output quality and information loss based on one data set. The results indicate that LLMs produce human-like process descriptions based on the predefined patterns, but apparently lack true comprehension of the process models.

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