1.Peculiarities of input data and test data:
Algorithm testing: For algorithms, input data is often the key to testing. Algorithms may produce different outputs for different types, distributions, and sizes of data. Test cases need to cover a variety of input scenarios, including boundary values, outliers, and special cases.
Routine testing: Routine software testing is usually more focused on functionality and system behavior, while changes in input data may be relatively small.
2.Unpredictable output:
Algorithm testing: The output of an algorithm may be numeric values, vectors, graphs, etc., and it is sometimes difficult to accurately anticipate the output. The test case needs to contain a review of the output resultTolerancewithApproximationVerification.
Routine testing: The output of conventional software is usually deterministic, easier to ** and verify.
3.Performance and efficiency requirements:
Algorithm testing: For large-scale datasets and complex algorithms, performance testing and efficiency testing are critical. The time complexity and spatial complexity of the algorithm need to be considered.
Routine testing: Routine software testing is usually more focused on the correctness of the functionality and has relatively low performance requirements.
4.Model Interpretability and Interpretability:
Algorithm testing: For machine learning and deep learning models, the explanatory and interpretability of the model is also part of the test. You need to verify that the model's decisions are as expected and that the model provides explainable results.
Routine testing: The behavior of regular software is generally easier to understand and does not require a special emphasis on interpretiveness.
5.Learning and tuning of algorithms:
Algorithm testing: For machine learning algorithms, the model may learn and adjust based on new data. Tests need to cover the model during the learning processStability and robustness
Routine testing: Conventional software typically operates under fixed rules and logic, with no dynamic learning and tweaking involved.
6.Diversity of datasets:
Algorithm testing: It is important to test datasets with different characteristics, distributions, and domains to ensure generalization and applicability of the algorithm.
Routine testing: Routine software testing may be more focused on specific use cases and inputs.
7.Model Evaluation Metrics:
Algorithm testing: Algorithm testing needs to focus on algorithm-specific evaluation metrics such as:Accuracy, recall, precision, f1 scoreWait.
Routine testing: Routine software testing usually uses more general metrics such as whether it is functioning properly, user experience, etc.
8.Testing Tools and Frameworks:
Algorithm testing: Algorithm testing may require the use of specialized testing tools and frameworks such as:tensorflow extended (tfx)、scikit-learnWait.
Routine testing: Routine software testing usually uses common testing frameworks such as Junit, PyTest, etc.
When testing algorithms, testing teams need to have an understanding of data science, machine learning, and domain knowledge. At the same time, as with conventional testing, testing methods such as automated testing, integration testing, and performance testing are still very valuable.