Test metrics and KPIs in Testing Fundamentals - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding Defect Density

Which of the following best describes the defect density metric in software testing?

A The number of defects found divided by the size of the software module, usually measured in lines of code.

B The total number of test cases executed during a test cycle.

C The percentage of test cases that passed successfully.

D The average time taken to fix a defect after it is reported.

💡 Hint

Think about how defects relate to the size of the software.

Defect density measures how many defects exist per unit size of software, helping teams understand quality relative to code size.

❓ Predict Output

intermediate

Calculating Test Execution Rate

What is the output of the following Python code that calculates the test execution rate?

Testing Fundamentals

total_tests = 150
executed_tests = 120
execution_rate = (executed_tests / total_tests) * 100
print(f"Execution Rate: {execution_rate:.2f}%")

A Execution Rate: 80.00%

B Execution Rate: 100.00%

C Execution Rate: 120.00%

D Execution Rate: 75.00%

💡 Hint

Divide executed tests by total tests and multiply by 100.

The code calculates the percentage of tests executed out of total tests, which is (120/150)*100 = 80.00%.

❓ assertion

advanced

Validating Test Pass Rate Assertion

Given the following test results, which assertion correctly verifies that the pass rate is at least 90%?

total_tests = 200
passed_tests = 185
pass_rate = (passed_tests / total_tests) * 100

A assert pass_rate > 90, f"Pass rate too low: {pass_rate}%"

B assert pass_rate <= 90, f"Pass rate too low: {pass_rate}%"

C assert pass_rate == 90, f"Pass rate too low: {pass_rate}%"

D assert pass_rate >= 90, f"Pass rate too low: {pass_rate}%"

💡 Hint

Check if pass rate is at least 90% (greater than or equal).

Option D correctly asserts that pass_rate is 90% or more. Option D excludes exactly 90%, C requires exact 90%, and D is incorrect logic.

🔧 Debug

advanced

Debugging Incorrect Defect Leakage Calculation

The following code is intended to calculate defect leakage percentage but produces an incorrect result. What is the main issue?

defects_found_in_testing = 30
defects_found_in_production = 5
defect_leakage = defects_found_in_production / defects_found_in_testing * 100
print(f"Defect Leakage: {defect_leakage}%")

A Division should be reversed: defects_found_in_testing / defects_found_in_production.

B The calculation is correct; no issue present.

C Integer division is used; convert to float to avoid truncation.

D The variable names are swapped; defects_found_in_production should be numerator.

💡 Hint

Check the formula for defect leakage: defects in production divided by defects found in testing.

The formula is correct: defect leakage = (defects in production / defects found in testing) * 100. Python 3 uses float division by default, so no truncation occurs.

❓ framework

expert

Choosing the Best KPI for Release Readiness

As a QA lead, you want to decide if a software release is ready based on test metrics. Which KPI is the most reliable indicator to include in your report?

A Percentage of automated test cases executed successfully.

B Number of defects found during testing.

C Defect severity distribution and open critical defects count.

D Total number of test cases designed.

💡 Hint

Consider which metric reflects risk and quality impact most directly.

Defect severity distribution and count of open critical defects provide insight into risk and quality, making it the best KPI for release readiness.