Why systematic techniques improve coverage in Testing Fundamentals - Challenge Your Understanding

Challenge - 5 Problems

🧠 Conceptual

intermediate

Why do systematic testing techniques improve test coverage?

Imagine you are testing a new app feature. Why does using a systematic testing technique help improve coverage compared to random testing?

A Systematic techniques test only the most popular features, ignoring less used parts to save time.

B Systematic techniques rely on guessing which parts might fail, so they test fewer areas but more deeply.

C Systematic techniques follow a planned approach that ensures all parts are tested, reducing missed areas.

D Systematic techniques avoid repeating tests, so they test fewer cases overall.

💡 Hint

Think about how planning helps cover all rooms in a house instead of just some.

Systematic techniques use a planned method to cover all parts of the software, reducing the chance of missing bugs in untested areas.

❓ Predict Output

intermediate

Output of coverage calculation with systematic vs random tests

Given two test sets for a function with 5 branches, one systematic covering all branches, one random covering some, what is the coverage percentage?

Testing Fundamentals

branches = ['A', 'B', 'C', 'D', 'E']
systematic_tests = ['A', 'B', 'C', 'D', 'E']
random_tests = ['A', 'C', 'E']

systematic_coverage = len(systematic_tests) / len(branches) * 100
random_coverage = len(random_tests) / len(branches) * 100

print(f"Systematic coverage: {systematic_coverage}%")
print(f"Random coverage: {random_coverage}%")

A

Systematic coverage: 100.0%
Random coverage: 60.0%

B

Systematic coverage: 80.0%
Random coverage: 60.0%

C

Systematic coverage: 100.0%
Random coverage: 40.0%

D

Systematic coverage: 60.0%
Random coverage: 60.0%

💡 Hint

Count how many branches each test set covers out of total branches.

Systematic tests cover all 5 branches (100%), random tests cover 3 out of 5 (60%).

❓ assertion

advanced

Which assertion best checks full coverage in a test report?

You have a test report object with a coverage field showing percentage coverage. Which assertion best confirms full coverage?

Testing Fundamentals

test_report = {'coverage': 100}

A assert test_report['coverage'] == 100

B assert test_report['coverage'] >= 90

C assert test_report['coverage'] > 100

D assert test_report['coverage'] != 0

💡 Hint

Full coverage means exactly 100%, not less or more.

Only assertion A checks that coverage is exactly 100%, confirming full coverage.

🔧 Debug

advanced

Find the bug in this coverage calculation code

What is wrong with this code that calculates coverage percentage?

Testing Fundamentals

def coverage_percentage(tested, total):
    return tested / total

print(coverage_percentage(80, 100))

A It raises a ZeroDivisionError if total is zero.

B It returns a decimal (0.8) instead of a percentage (80).

C It divides total by tested, causing wrong results.

D It returns a string instead of a number.

💡 Hint

Think about how percentages are usually shown.

The function returns a fraction (0.8) but coverage is usually shown as a percentage (80%).

❓ framework

expert

Which test framework feature best supports systematic coverage?

In a test framework, which feature helps ensure systematic coverage of all input cases?

A Random test order to find flaky tests.

B Using print statements for manual result checking.

C Skipping tests to save time on less important cases.

D Parameterization to run tests with multiple input values automatically.

💡 Hint

Think about how to run the same test many times with different inputs.

Parameterization allows running tests with all planned inputs, supporting systematic coverage.