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]

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

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

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

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)

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

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().

Practice

1.

What is the main purpose of regression testing in data science projects?

easy

A. To check that new changes do not break existing results

B. To create new models from scratch

C. To collect new data for training

D. To visualize data trends

Solution

1. Step 1: Understand regression testing goal

   Regression testing is about verifying that recent changes do not cause errors in existing functionality.

2. Step 2: Match purpose with options

   Only To check that new changes do not break existing results describes this goal correctly, others describe unrelated tasks.

3. Final Answer:

   To check that new changes do not break existing results -> Option A

4. Quick Check:

   Regression testing = check no breakage [OK]

Hint: Regression testing ensures old results still work after changes [OK]

Common Mistakes:

• Confusing regression testing with model building
• Thinking it collects new data
• Assuming it is for data visualization

2.

Which of the following is the correct way to describe regression testing?

Regression testing:

def regression_test(old_output, new_output):
    return old_output == new_output

easy

A. It compares old and new outputs to detect changes

B. It trains a new model using old and new data

C. It visualizes differences between datasets

D. It deletes old test results before running new tests

Solution

1. Step 1: Analyze the function code

   The function returns True if old_output equals new_output, meaning it compares outputs.

2. Step 2: Match function behavior to options

   It compares old and new outputs to detect changes correctly states it compares outputs to detect changes; others describe unrelated actions.

3. Final Answer:

   It compares old and new outputs to detect changes -> Option A

4. Quick Check:

   Function compares outputs = It compares old and new outputs to detect changes [OK]

Hint: Regression test checks if old and new outputs match exactly [OK]

Common Mistakes:

• Thinking regression testing retrains models
• Assuming it creates visualizations
• Believing it deletes old results

3.

Given the following Python code for regression testing outputs, what will be the printed result?

old_results = [10, 20, 30]
new_results = [10, 20, 25]

if old_results == new_results:
    print("Test Passed")
else:
    print("Test Failed")

medium

A. Test Passed

B. SyntaxError

C. Test Failed

D. No output

Solution

1. Step 1: Compare the two lists element-wise

   old_results has [10, 20, 30], new_results has [10, 20, 25]. The last elements differ (30 vs 25).

2. Step 2: Evaluate the if condition

   Since the lists are not equal, the else block runs, printing "Test Failed".

3. Final Answer:

   Test Failed -> Option C

4. Quick Check:

   Lists differ, so output = Test Failed [OK]

Hint: Lists must match exactly for test to pass [OK]

Common Mistakes:

• Assuming partial match passes test
• Confusing list equality with set equality
• Expecting syntax errors from correct code

4.

Identify the error in this regression testing code snippet:

def regression_test(old, new):
    if old = new:
        return True
    else:
        return False

medium

A. Missing return statement

B. Using assignment operator '=' instead of comparison '==' in if condition

C. Function should print results, not return

D. Incorrect function name

Solution

1. Step 1: Check the if condition syntax

   The condition uses '=' which is assignment, not comparison. Python requires '==' for comparison.

2. Step 2: Identify correct syntax for comparison

   Replacing '=' with '==' fixes the syntax error and allows proper comparison.

3. Final Answer:

   Using assignment operator '=' instead of comparison '==' in if condition -> Option B

4. Quick Check:

   Use '==' to compare, not '=' [OK]

Hint: Use '==' for comparison, '=' is assignment [OK]

Common Mistakes:

• Confusing '=' and '==' in conditions
• Thinking return is missing
• Believing function name matters for syntax

5.

You have a data science model that outputs predictions as a list. You want to automate regression testing to check if new code changes break the model's output. Which approach below best applies regression testing?

hard

A. Delete old predictions and start fresh after every change

B. Train a new model with updated data and ignore old predictions

C. Visualize new predictions only to check for differences

D. Save previous predictions and compare them with new predictions after changes

Solution

1. Step 1: Understand regression testing in model outputs

   Regression testing means re-running tests and comparing new outputs to old outputs to detect unintended changes.

2. Step 2: Evaluate options for best fit

   Save previous predictions and compare them with new predictions after changes saves old predictions and compares them to new ones, matching regression testing. Others ignore old outputs or delete them, which is not regression testing.

3. Final Answer:

   Save previous predictions and compare them with new predictions after changes -> Option D

4. Quick Check:

   Regression testing = compare old and new outputs [OK]

Hint: Keep old outputs to compare with new after changes [OK]

Common Mistakes:

• Ignoring old outputs in testing
• Assuming visualization replaces regression tests
• Deleting old results before testing