Analysis of an Intelligent Deep learning-driven automated framework for functional testcase generation, execution and flaky test detection with self-repair mechanism

Efficient software testing is essential for productive software development and reliable user experiences. As human testing is inefficient and expensive, automated software testing is needed. A deep learning-driven automated framework for functional testing shifts software QA from rigid, script-based execution to an intelligent ecosystem capable of human-like visual perception, natural language understanding, and self-healing resilience. A deep learning-driven automated framework for functional testing shifts software quality assurance from fragile, hard-coded scripts to adaptive, intent-based automation. By utilizing computer vision, sequence models, and reinforcement learning, these frameworks automatically discover user interface (UI) elements, author test cases, and repair broken paths. Algorithmic techniques include machine learning, neural networks, and decision trees, and their application in the generation, execution, and debugging of test cases is analysed. AI-based frameworks use ML as the core basis for their approach, which breaks down this level of complexity by code base analysis, historical bug data, and user behaviour pattern identification. While Automation enhances speed, consistency, and test coverage, flaky test cases can significantly undermine these benefits. A flaky test might pass or fail inconsistently, even without changes to the application or test script, leading to confusion and wasted resources.