AI Integrated ST Modern System for Designing Automated Standard Affirmation System

This is a phenomenal approach toward a game-changer in AI-driven software testing and assures quality automation of the software product, facilitating faster, more exact, and very comprehensive checking of the software product. This would present a very solid architecture that would lay the ground for leveraging the capabilities of artificial intelligence (AI) in the automation and enhancement of the software testing process. The framework assumes that AI integrated with software testing can make a giant leap in test coverage, precision, and efficiency, which eventually ensures software with higher quality. A number of key constituents, each with the aim of tackling specific challenges that the software testing lifecycle poses, join to form the foundation of the Mitra framework. It includes at its very base machine learning (ML) algorithms and natural language processing (NLP) techniques that automatically produce and optimize test cases. Not only does this bring down the manual effort in creating test cases, but it also betters the detection capability for not-identified cases earlier, especially edge cases, which helps in bettering the test coverage. Other than that, the very important feature of the framework includes AI for better predictive analysis. The system is able to predict potential future software defects based on the historical data of software defects and test results, hence alerting the testers at an early time for correction before the issues become bigger defects. This predictive capability helps reduce the investment of time and resources taken in finding and fixing defects and further provides the ability to prioritize testing efforts considering their likelihood and the severity of potential defects.