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

2025-07-17

AI Summary

Pretrained-model-driven software systems exhibit emergent properties—including ill-defined capability boundaries, strong contextual dependency of behavior, and continuous evolution—that challenge foundational assumptions in traditional requirements engineering, particularly functional decomposability and behavioral predictability. To address this, this study proposes the first systematic conceptual framework for requirements engineering tailored to such systems, integrating AI-systems analysis with established software requirements theory to enable conceptual modeling and methodological reconstruction. The framework transcends static functional decomposition by explicitly modeling context sensitivity, capability uncertainty, and evolutionary adaptability. It provides a rigorous theoretical foundation for academia and actionable guidance for industry—enabling requirements modeling, verification, and evolution that seamlessly integrate pretrained models. Furthermore, it identifies critical research directions toward “AI-native” requirements engineering. (149 words)

Technology Category

• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Knowledge Representation and Reasoning: Preferences
• Multiagent Systems: Agent/AI Theories and Architectures

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

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.

Problem

• Address ambiguous capability boundaries in pretrained-model systems
• Handle context-dependent behavior of pretrained-model systems
• Adapt requirements engineering for continuous model evolution

Innovation

• Framework for pretrained-model requirements engineering
• Addresses ambiguous capability and behavior
• Supports continuous model evolution adaptation