Using Machine Learning to predict vulnerabilities in software testing

This white paper examines how machine learning (ML) can improve testing methods. It also discusses the differences in the modern approach to testing which involves both automation and manual testing, and how testing can be improved using machine learning algorithms.

Highlights from the paper include:

• ML's role in automating test case creation, script upkeep, and defect forecasting to boost efficiency
• Using Bayes' Theorem for pinpointing high-risk areas in testing
• ML in testing shortening regression periods from weeks to days

Discover more in the full report.