FAIRE Framework: A Formal Architecture for Emergent Requirements in AI-Amplified Software Engineering
FAIRE Framework
DOI:
https://doi.org/10.66660/ghyn.v1i12.131Keywords:
Requirements Engineering · Human-AI Collaboration · Emergent Requirements · Large Language Models · Software Governance · Formal MethodsAbstract
Tegrated LLMs into requirements engineering shift how requirements are elicited, analyzed, and documented, cutting task completion time by 30–40% across RE activities. Hoever, no systematic mechanism yet governs requirements emerging from human–AI collaboration. This research introduces the FAIRE Framework (Formal Architecture for Institutionalizing and Regulating Emergence), a mathematically grounded, four-layer architecture for detecting, measuring, governing, and tracing emergent requirements in AI-amplified development.
The research follows a design science methodology, progressing through a systematic literature review, formal specification using state machines and temporal logic, prototype implementation across contemporary LLM APIs (GPT-4, Claude, LLaMA, Gemini), and empirical evaluation through controlled experiments (N = 41) and longitudinal case studies.
Results show FAIRE reduces undocumented requirement emergence by 79.7% (15.8 to 3.2 events per project; p < 0.001), improves traceability completeness from 42.1% to 89.3% (p < 0.001), and achieves 94.2% recall in detecting AI-introduced behaviors requiring stakeholder review. Overhead is modest (8.7 minutes per 4-hour session), and governance value is rated highly (SUS = 74.2).
To the best of our knowledge, FAIRE is among the first formal architectures for governing emergent requirements in AI-amplified environments, addressing gaps in reproducibility, interpretability, and accountability. It offers organizations a practical mechanism for leveraging AI's generative potential while maintaining engineering discipline.
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Copyright (c) 2026 Abdelaziz Omran Abdussalam
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