AI VIRTUAL TEST PREFLIGHT USING SOURCE CODE AND ENVIRONMENT MODELS

Inventor(s)

• Raji Vinayagam
• Sajeev Ramachandran
• Shivram Salem Arunachalam

Abstract

This proposal introduces an AI-driven virtual test preflight system that predicts test-blocking issues before execution by combining structured test intent, environment constraints, and reachable code path analysis. By triangulating a Test Plan, an Environment Model, and a Code Graph, the proposed system identifies likely failures early and provides explainable, test-step-specific predictions without requiring full runtime execution.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.

Recommended Citation

Vinayagam, Raji; Ramachandran, Sajeev; and Arunachalam, Shivram Salem, "AI VIRTUAL TEST PREFLIGHT USING SOURCE CODE AND ENVIRONMENT MODELS", Technical Disclosure Commons, (April 16, 2026)
https://www.tdcommons.org/dpubs_series/9807