Risk-Based Testing is an approach that focuses testing efforts on areas of a system that pose higher risks to the project's success. It involves identifying potential risks, assessing their impact and likelihood, and prioritizing testing activities accordingly. By allocating resources based on risk, it ensures efficient test coverage and maximizes the likelihood of detecting critical defects. This approach allows organizations to mitigate risks early, make informed decisions, and deliver high-quality software that meets customer expectations while optimizing testing efforts and resources.

Risk-Based Testing leverages artificial intelligence (AI) technologies to enhance the risk-based testing approach. It utilizes machine learning algorithms to analyze vast amounts of data, including historical testing results, defect patterns, and project metrics. Automating risk assessment and prioritization enables more accurate identification of high-risk areas, optimizing testing efforts and resources for efficient and effective testing. Risk-Based Testing AI improves decision-making accelerates defect detection, and enhances overall testing outcomes, leading to higher-quality software products.