A Conceptual Framework for Requirements Engineering of Pretrained-Model-Enabled Systems

TL;DR

This paper introduces a new conceptual framework for requirements engineering tailored to systems that incorporate large pretrained models, addressing their unique challenges like ambiguity and evolution.

Contribution

It proposes a novel framework specifically designed for requirements engineering of pretrained-model-enabled systems, guiding future research and practice.

Findings

Identifies key challenges posed by pretrained models in requirements engineering

Proposes a conceptual framework to address these challenges

Outlines promising research directions for future work

Abstract

Recent advances in large pretrained models have led to their widespread integration as core components in modern software systems. The trend is expected to continue in the foreseeable future. Unlike traditional software systems governed by deterministic logic, systems powered by pretrained models exhibit distinctive and emergent characteristics, such as ambiguous capability boundaries, context-dependent behavior, and continuous evolution. These properties fundamentally challenge long-standing assumptions in requirements engineering, including functional decomposability and behavioral predictability. This paper investigates this problem and advocates for a rethinking of existing requirements engineering methodologies. We propose a conceptual framework tailored to requirements engineering of pretrained-model-enabled software systems and outline several promising research directions within this framework. This vision helps provide a guide for researchers and practitioners to tackle the emerging challenges in requirements engineering of pretrained-model-enabled systems.