The Power of Machine Learning and Artificial Intelligence Testing

Data Validation

• Evaluation of quality of training datasets, including aspects such as bias and variety
• Validation of data labels
• Exaction and curation of validation and test datasets

AI Model Evaluation and Testing

• Model’s prediction result analysis and evaluation
• AI model evaluation is based on metrics like confusion matrix, AUC ROC, F1 Score, etc.
• Sharing insights and feedback on model behavior to AI model developers

Computer Vision Application Testing

• Thorough QA and QC for images or video data ingestion
• Testing of data annotations, data labeling, and data ingestion format
• Computer vision-based test automation for visual testing

NLP Applications Testing

• Testing of NLP models’ recognition and predictions
• Speech and NLP models evaluation based on metrics like word error rate (WER), text similarity measures such as cosine similarity, Levenshtein distance, etc

Metamorphic Testing

• Test case generation and test results verification based on metamorphic relations, to validate the algorithm’s response to multiple inputs and their expected outputs
• Testing metamorphic relations

Non-functional Testing

• Data scalability and performance testing for the AI systems
• System integration and API Testing
• Security Testing

Chatbot Testing

Domain Testing: Chatbots are domain-specific and need to have specificities associated with their domain identified and tested upfront.

Limit Testing: Verification of how a chatbot responds to an irrelevant question and identifying the outcome when a chatbot fails.

Robotics Testing

• It is a simulation-based behavior testing that includes debugging an algorithm, testing object detection and response, and testing defined goals.
• Testing of hardware availability and unavailability scenarios.