Data Analysis and Pre-processing: Collect, prepare and work with large datasets to identify patterns and extract metrics for modeling. Model Training: Train models to analyze software changes and predict testing needs. Implementation and Testing: Automated processes. Implement models in a real-world environment and continuously test and refine them based on feedback and results. Collaboration: Work closely with software development, integration and testing teams to understand requirements and integrate machine learning insights into the software delivery cycle. Documentation: Document the development process, model architectures, and experiment outcomes.