The scope of Artificial intelligence AI in software testing is broad and encompasses various aspects of AI system development and deployment. Here are some key areas within the scope of testing in AI: Data Testing: AI systems heavily rely on data for training, testing, and validation. Data testing involves ensuring the quality, completeness, and accuracy of the training data. It includes data preprocessing, data validation, and data augmentation techniques. Model Testing: This involves testing the AI model or algorithm itself. It includes verifying the model’s performance, accuracy, and robustness across different scenarios and input data. Model testing may involve evaluating metrics such as precision, recall, accuracy, F1-score, and analyzing the model’s behavior under different conditions. Performance Testing: AI systems often deal with large volumes of data and complex computations. Performance testing focuses on assessing the system’s speed, responsiveness, scalability, and resource utilization. It helps identify potential bottlenecks, optimize algorithms, and…
