Traditional system testing and ML-based system testing differ significantly due to the nature of the systems they evaluate. Here’s a breakdown of the differences between the two:
Traditional system testing focuses on evaluating deterministic systems where the behavior is predefined and predictable based on the input. It includes:
- Unit Testing:? Tests individual components or functions in isolation. Results are binary (pass/fail) based on expected outcomes.
- Integration Testing: Tests interactions between integrated units or components to ensure they work together as intended. Tests are deterministic, with expected input and output.
- System Testing: Tests the entire system as a whole to verify that it meets specified requirements. Involves end-to-end testing in a complete environment, ensuring all system components work together. Again, results are deterministic.
ML-based system testing includes traditional testing components but adds additional layers due to the unique nature of machine learning models. ML systems are often probabilistic and data-driven, which introduces new challenges. ML-based system testing includes:
- Unit Testing: Focuses on testing individual functions or methods within the ML pipeline, such as data preprocessing functions or feature extraction.
- Integration Testing: Ensures that different components of the ML pipeline (like data ingestion, feature engineering, and model training) work well together.
- System Testing: Evaluates the entire ML application, ensuring it performs correctly in a real-world scenario.
Specifically tests the machine learning model itself. Key characteristics -
- Accuracy & Performance: Evaluates the model’s accuracy, precision, recall, F1-score, and other metrics to ensure it performs as expected on training, validation, and test datasets.
- Robustness: Tests how the model performs with outliers, noisy data, or adversarial inputs to ensure it generalizes well.
- Bias & Fairness: Checks for any biases in the model’s predictions, ensuring fairness across different demographic groups or classes.
- Explainability and Interpretability: Tests how well the model’s predictions can be explained or interpreted (transparency & trust)
Tests the infrastructure and environment where ML model is deployed. Key characteristics -
- Scalability: Ensures the system can handle increased loads - more users, more data.
- Monitoring & Logging:? Ensures proper monitoring for model performance (like drift detection) and logging for auditing and debugging purposes.
- Data pipeline testing: Ensures that the data pipeline from ingestion to preprocessing and feeding into the model works as intended.
- Deployment testing: Tests deployment pipelines for automating the release of new models
