Create a system that uses machine learning algorithms to look at old defect data and guess what bugs will happen in the future. The project involves cleaning up the data, training machine learning models, and using predictive analytics to make tests more thorough and lower the number of defects in production. This project in the real world helps with risk-based testing and makes software quality assurance more efficient.

Use AI tools like Test.ai, Applitools, or Selenium with AI plugins to make a framework for self-healing automation. When the UI changes, the system should automatically update locators. This will make it easier to keep tests up to date. This project is about smart test execution, automatic bug detection, and adaptive testing strategies for enterprise applications that can grow.

Make a tool that uses NLP to turn natural language requirements into test cases that can be run. The system uses Natural Language Processing to read user stories, make test scenarios, and run them. This project shows how AI, ML, and NLP can be used in software testing to speed up requirement validation and make test automation better.