Regression testing in Testing Fundamentals - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the regression testing module.

Testing Fundamentals

import [1]

Drag options to blanks, or click blank then click option'

Amock

Bunittest

Cpytest

Dregression_testing

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'unittest' which is a testing framework but less common for regression testing.

Trying to import a non-existent module named 'regression_testing'.

We use pytest for regression testing in Python because it supports test automation and easy test writing.

2fill in blank

medium

Complete the code to define a regression test function that checks if the output matches expected.

Testing Fundamentals

def test_model_output():
    result = model.predict(data)
    assert result == [1]

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ANone

Bdata

Cmodel

Dexpected_output

Attempts:

3 left

💡 Hint

Common Mistakes

Comparing result to the input data instead of expected output.

Using None which would always fail the test.

The test compares the model's prediction result to the expected_output to detect regressions.

3fill in blank

hard

Fix the error in the test assertion to correctly check if the model's accuracy is above 90%.

Testing Fundamentals

def test_accuracy():
    accuracy = model.evaluate(test_data)
    assert accuracy [1] 0.9

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A>=

B<

C==

D<=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' which would fail even if accuracy is good.

Using '==' which is too strict for floating point values.

The test should assert that accuracy is greater than or equal to 0.9 to pass.

4fill in blank

hard

Fill both blanks to create a regression test that filters results and checks if all are above threshold.

Testing Fundamentals

filtered_results = [r for r in results if r [1] threshold]
assert all(r [2] threshold for r in filtered_results)

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A>

B>=

C<

D<=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' in the filter which selects wrong results.

Using '<=' in the assertion which may allow lower values.

The list comprehension filters results greater than threshold, and the assertion checks all are greater or equal to threshold.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps features to values only if value is above limit.

Testing Fundamentals

filtered_dict = [1]: [2] for [3], [2] in features.items() if [2] > limit}

Drag options to blanks, or click blank then click option'

Afeature

Bvalue

Ck

Dv

Attempts:

3 left

💡 Hint

Common Mistakes

Using different variable names inconsistently causing errors.

Not matching the key and value names in the comprehension.

The comprehension uses feature as key and value as value, iterating over features.items().