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

TL;DR

This paper introduces Agentic AI simulations for Requirements Engineering, providing a new research tool to study the effects of requirement defects and AI-influenced requirements quality, supported by a prototype and feasibility study.

Contribution

It presents the first concept, research roadmap, prototype, and initial feasibility study for agentic AI-based RE simulations, highlighting their potential and limitations.

Findings

Simulations are executable with naive implementation

Agentic AI can model RE processes effectively

Encourages technical improvements and broader application

Abstract

Context and motivation. Requirements Engineering (RE) quality still lacks empirical evidence on how specific requirement defects affect downstream activities. 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 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 na\"ive implementation leads to executable simulations, encouraging technical improvements along with broader application in RE research.