At the Testμ Conference, Soumya Mukherjee, an Engineer Manager at Apple, highlighted the importance of implementing a test recommender system to streamline software testing processes by efficiently identifying tests impacted by code changes. He emphasized the need for probabilistic models to correlate test failures with specific code changes, using machine learning techniques like gradient-boosted decision trees, and leveraging tools for code instrumentation and coverage. Mukherjee explained the challenges of traditional test prediction methods, which often misidentify flaky tests, and proposed a model-based approach to improve accuracy and reduce test cycle times. He addressed audience questions about tool selection, model biases, and data automation, underscoring the need for standardized data formats and constant model learning to adapt to dynamic testing environments. Mukherjee also mentioned the role of LambdaTest, a cloud platform facilitating continuous quality testing, in supporting these advanced testing methodologies.