Notes for B.1.5 Designing Test Cases - IB | RevisionDojo
B.1.5 Designing Test Cases Notes
Why Test Cases Matter
Test cases are essential for evaluating the performance and reliability of an model.
They help ensure that the model behaves as expected in different scenarios.
This can be brokendown into three components:
Validate Accuracy: Ensure the model produces correct predictions.
Identify Weaknesses: Reveal areas where the model may fail.
Ensure Robustness: Test the model's ability to handle unexpected or edge cases.
Types of Test Cases
Test cases can be categorized into several types:
Positive test cases check if the model performswell on typical, expected inputs.
Negative test caseschallenge the model with unusual or adversarial inputs.
Edge cases test the model's behavior on inputs at the boundaries of its capabilities.
These cases simulate real-world conditions to ensure the model's practical usability.
Example
For a spam filter, positive test cases might include clear spam emails and legitimate messages.
For the same spam filter, negative test cases could include emails with mixed spam and legitimate content.
Edge cases can include testing a language model with extremely long or short sentences.
Designing Effective Test Cases
Understand the model's purpose
Clearly define what the model is supposed to do.
Identify Key Scenarios
List the scenarios that the model will encounter in real-world use.
Cover a Range of Inputs
Ensure your test cases include a variety of inputs, from typical to rare cases.
Define Expected Outcomes
For each test case, specify the expected result.
Automate Testing
Whenever possible, automate your testcases to ensureconsistent and repeatable evaluations.
Tip
When designing test cases, it is a goodpractice to ask yourself: "What are the key tasks the model should perform?"
To automatetesting use testing frameworks like pytest for Python models as it simplifies the process.
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Note
Introduction to Test Cases
Test cases are essential for evaluating the performance and reliability of a model.
They help ensure that the model behaves as expected in different scenarios.
This can be broken down into three components:
Validate Accuracy: Ensure the model produces correct predictions.
Identify Weaknesses: Reveal areas where the model may fail.
Ensure Robustness: Test the model's ability to handle unexpected or edge cases.
AnalogyThink of test cases as a safety net for your model, catching any errors or unexpected behaviors before they reach users.
ExampleFor a language translation model, test cases might include translating common phrases, rare idioms, and sentences with ambiguous meanings.
