The document discusses using machine learning (ML) to enhance software testing by predicting vulnerabilities early in the process. It explains how ML algorithms, like Bayes' Theorem, can anticipate high-risk areas, optimize test case generation, and reduce testing timelines. By leveraging historical data and patterns, ML allows for more efficient regression testing and resource allocation, improving software reliability and reducing risks. This approach strengthens software quality assurance by proactively addressing issues before they escalate, making it a crucial tool in modern software development.

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