IV&V Requirements Quality & Traceability Analysis
Description
This project develops software to generate pre-computed, draft analysis results for text-based software requirements artifacts on NASA programs within the scope of Independent Verification & Validation (IV&V). The software provides a web-based interface that enables users to filter pre-computed findings by issue severity, evaluate generative AI–produced results, and auto-draft IV&V issues. Its analysis capabilities include assessment of requirement quality attributes, decomposition of functionality, and evaluation of both upward and backward traceability. Initial development and testing leverage synthetic data hosted on AWS (Cloud – Other). Beginning in FY26, the project will transition to beta and production phases, operating on local on-premises HPC infrastructure with access to real ITAR-level NASA mission data.
Detailed example
The AI system (Web UI-based) outputs requirements, their rationale, assessments (recommendations) for quality, decomposition, and traceability, and flags through color coding. Analysts will also have the opportunity to autogenerate draft issues, provide feedback on the results through buttons and comment boxes, and export results to a spreadsheet.
AI / analytics pattern
Generative AI: AI that generates new or synthetic content (e.g., images, videos, audio, text, code).
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
Implementation of the local on-premises HPC environment and subsequent execution of AI-enhanced IV&V analysis activities will begin in FY26. These activities will be complemented by expert human engineering judgement and further analysis. By integrating AI-generated content into NASA IV&V's current processes, analysis results and assessments can be produced significantly faster than with traditional methods. This accelerated capability is expected to positively impact the mission projects supported by NASA IV&V by enabling earlier identification of issues, more thorough characterization of residual risk and impact of any open identified issues, and delivery of improved, risk-informed decision-making support to programs.
Controls / human review
ATO: Not reported; PIA: Not published