Partha Sarathi Samal , Suresh Kumar Palus and Sai Kiran Padmam , Independent Researcher , USA

Automated testing tools that use machine learning and AI have made great strides, they still struggle to keep up with fast-changing user interfaces and unpredictable application behavior, often requiring a lot of manual updates and maintenance. This paper introduces a smarter approach-an Adaptive NLP-Based Test Automation Framework-that uses natural language processing (NLP), machine learning, and language understanding to build test cases that can adjust and repair themselves automatically. This system can read test instructions written in plain language, turn them into reliable test scenarios, spot changes in the user interface, and update test logic on the fly-without needing a human to step in.
By combining advanced language models, entity recognition, and relationship mapping, the framework can cut test maintenance by up to 80%, while improving both accuracy and coverage. The paper walks through the system’s design, the NLP technologies it uses, how it's implemented, and how it performs in large, complex enterprise environments. It also tackles key technical challenges like how efficiently the models can be trained, how understandable their decisions are, and how well the system fits into existing DevOps pipelines. Based on thorough testing and real-world examples, the study shows that this NLP-powered approach could mark a major step forward in building smarter, more flexible software testing systems that can keep up with modern business needs.

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