AI-Assisted Definition of Non-Functional Requirements for Commerce Platforms

A peer-reviewed study published in March 2025 by researchers at the Rochester Institute of Technology provides the most rigorous evaluation of AI-generated NFRs to date. The research team developed a framework using eight large language models to derive quality-driven NFRs from 34 functional requirements drawn from a dataset of 3,964 specifications. The LLMs generated 1,593 NFRs based on the ISO/IEC 25010:2023 standard. Ten industry software quality evaluators assessed a stratified sample and recorded median validity and applicability scores of 5.0 out of 5.0, with mean scores of 4.63 and 4.59 respectively. The attribute classification accuracy reached 80.4%, with only 11.3% complete mismatches, demonstrating that LLMs can automate NFR elicitation in over 80% of cases while reducing manual effort.

In an industrial context, a case study at the German technology firm Bosch evaluated IBM's Requirements Quality Assistant against manual expert review for automotive requirements specifications. The tool completed analysis of full requirements sets and documented findings within the requirements management system in a fraction of the time required for manual inspection, which typically takes approximately one week for a formal review. However, the study found that the tool's syntactical checks, based on INCOSE guidelines, did not fully address the semantic quality criteria that Bosch engineers prioritized, including completeness and consistency of content. The researchers recommended that tool vendors integrate semantic checks alongside syntactical analysis to increase practical utility in domain-specific contexts.