Adaptive Natural Language Processing-Based Test Automation Framework: Enabling Self-Healing And Context-Aware Test Cases

Partha Sarathi Samal¹, Suresh Kumar Palus ², Sai Kiran Padmam ³ , ¹ Independent ResearcherConnecticut, USA ,² Independent Researcher Pennsylvania, USA , ³Independent Researcher New Jersey, USA

ABSTRACT

This study presents the CYMC Multimedia Progression Model (MPM), a technology-enhanced framework developed across the California Youth Music Competition’s regional and international tiers. Because CYMC administers all levels of competition, it implements unified, high-standard multimedia recording that supports both reflective learning and advancement. Drawing on more than 200 professionally captured performance videos from young musicians, the study examines how standardized multimedia documentation functions as a digital portfolio for international selection, enabling students to progress from local performances to CYMC’s global stages. Findings indicate that this multimedia-supported cycle enhances self-efficacy, expressive behavior, and goal-directed motivation. The upward pathway—where each regional performance carries the potential for international exposure—creates a strong motivational loop and strengthens learners’ artistic identity. The model offers a scalable technology-driven approach for developing confidence, resilience, and engagement in youth performance education.

Keywords

Natural Language Processing, Test Automation, Self-Healing Tests, Machine Learning, Semantic Analysis, AI-Driven QA, Test Maintenance, Context-Aware Testing, Named Entity Recognition, Specification-Driven Development