The Software Engineering Simulations Lab: Agentic AI for RE Quality Simulations

2025-11-21

AI Summary

Requirement quality assessment in requirements engineering (RE) has long relied on expert intuition, lacking empirical, quantifiable foundations; moreover, conventional quality models fail to accommodate AI-agent–augmented development paradigms. Method: This study pioneers the application of agent-based modeling (ABM) to RE quality research, introducing a simulation environment tailored for the AI era. We propose an extensible conceptual framework and technical pipeline integrating stochasticity, dynamism, and event-driven mechanisms to automate qualitative simulation of how requirement defects propagate and impact the entire software engineering lifecycle. Contribution/Results: A preliminary prototype validates feasibility, generating executable simulation workflows. The work shifts RE quality assessment from experience-driven to simulation-driven practice and lays the groundwork for data-informed, AI-ready quality models.

Technology Category

• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Knowledge Representation and Reasoning: Qualitative Reasoning

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

Context and motivation. Quality in Requirements Engineering (RE) is still predominantly anecdotal and intuition-driven. Creating a solid requirements quality model requires broad sets of empirical evidence to evaluate quality factors and their context. Problem. However, empirical data on the detailed effects of requirements quality defects is scarce, since it is costly to obtain. Furthermore, with the advent of AI-based development, the requirements quality factors may change: Requirements are no longer only consumed by humans, but increasingly also by AI agents, which might lead to a different efficient and effective requirements style. Principal ideas. We propose to extend the RE research toolbox with Agentic AI simulations, in which software engineering (SE) processes are replicated by standardized agents in stochastic, dynamic, event-driven, qualitative simulations. We argue that their speed and simplicity makes them a valuable addition to RE research, although limitations in replicating human behavior need to be studied and understood. Contribution. This paper contributes a first concept, a research roadmap, a prototype, and a first feasibility study for RE simulations with agentic AI. Study results indicate that even a naive implementation leads to executable simulations, encouraging technical improvements along with broader application in RE research.

Problem

• Empirical data on requirements quality defects is scarce
• AI agents may change effective requirements quality factors
• Simulations replicate software processes with agentic AI

Innovation

• Agentic AI simulations for RE quality analysis
• Standardized agents replicate SE processes dynamically
• Stochastic event-driven simulations enable rapid RE research