Defect metrics in Testing Fundamentals - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the defect density.

Testing Fundamentals

defect_density = total_defects / [1]

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

Atotal_lines_of_code

Btotal_test_cases

Ctotal_modules

Dtotal_testers

Attempts:

3 left

💡 Hint

Common Mistakes

Using total_test_cases instead of total_lines_of_code

Dividing by total_modules which is unrelated

Defect density is calculated as the number of defects divided by the total lines of code.

2fill in blank

medium

Complete the code to calculate the defect removal efficiency (DRE).

Testing Fundamentals

DRE = defects_removed_during_testing / [1]

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

Atotal_defects_found

Bdefects_removed_after_release

Cdefects_found_in_production

Dtotal_defects

Attempts:

3 left

💡 Hint

Common Mistakes

Using only defects found during testing as denominator

Using defects found in production only

DRE is the ratio of defects removed during testing to the total defects (found before and after release).

3fill in blank

hard

Fix the error in the code to calculate the defect leakage rate.

Testing Fundamentals

defect_leakage = defects_found_in_production / [1]

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

Adefects_found_during_testing

Btotal_defects

Cdefects_found_in_production

Ddefects_removed_during_testing

Attempts:

3 left

💡 Hint

Common Mistakes

Dividing by defects found during testing only

Dividing by defects found in production (same numerator and denominator)

Defect leakage rate is defects found in production divided by total defects.

4fill in blank

hard

Fill both blanks to calculate the defect removal efficiency as a percentage.

Testing Fundamentals

DRE_percentage = ([1] / [2]) * 100

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

Adefects_removed_during_testing

Btotal_defects

Cdefects_found_in_production

Ddefects_removed_after_release

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping numerator and denominator

Using defects found in production as numerator

DRE percentage is defects removed during testing divided by total defects, multiplied by 100.

5fill in blank

hard

Fill all three blanks to calculate defect density per thousand lines of code.

Testing Fundamentals

defect_density = ([1] / [2]) * [3]

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

Atotal_defects

Btotal_lines_of_code

C1000

D100

Attempts:

3 left

💡 Hint

Common Mistakes

Multiplying by 100 instead of 1000

Swapping numerator and denominator

Defect density per KLOC is total defects divided by total lines of code, multiplied by 1000.

Practice

1. What does defect density measure in software testing?

easy

A. Time taken to fix a defect

B. Total number of test cases executed

C. Number of defects per size of the software

D. Number of users reporting defects

Solution

1. Step 1: Understand defect density meaning

   Defect density is a metric that counts defects relative to software size, like per thousand lines of code.

2. Step 2: Compare options with definition

   Only Number of defects per size of the software matches this definition; others relate to different metrics.

3. Final Answer:

   Number of defects per size of the software -> Option C

4. Quick Check:

   Defect density = defects / software size [OK]

Hint: Defect density = defects divided by software size [OK]

Common Mistakes:

• Confusing defect density with total defects
• Mixing defect density with test execution count
• Thinking defect density measures fix time

2. Which of the following is the correct formula for calculating defect density?

easy

A. Defect Density = Total Defects / Software Size

B. Defect Density = Software Size / Total Defects

C. Defect Density = Total Defects x Software Size

D. Defect Density = Total Defects + Software Size

Solution

1. Step 1: Recall defect density formula

   Defect density is calculated by dividing total defects by software size.

2. Step 2: Match formula with options

   Defect Density = Total Defects / Software Size correctly shows division of defects by size; others use incorrect operations.

3. Final Answer:

   Defect Density = Total Defects / Software Size -> Option A

4. Quick Check:

   Defect density = defects ÷ size [OK]

Hint: Defect density divides defects by size, not multiply [OK]

Common Mistakes:

• Multiplying defects by size instead of dividing
• Reversing numerator and denominator
• Adding defects and size instead of dividing

3. A software module has 1200 lines of code and 24 reported defects. What is the defect density?

medium

A. 20 defects per 1000 lines

B. 50 defects per 1000 lines

C. 0.02 defects per line

D. 0.02 defects per 1000 lines

Solution

1. Step 1: Calculate defects per 1000 lines

   Defect density = (24 defects / 1200 lines) x 1000 = 20 defects per 1000 lines.

2. Step 2: Compare with options

   20 defects per 1000 lines matches 20 defects per 1000 lines; others are incorrect calculations or units.

3. Final Answer:

   20 defects per 1000 lines -> Option A

4. Quick Check:

   (24/1200)*1000 = 20 [OK]

Hint: Multiply defects/lines by 1000 for defect density [OK]

Common Mistakes:

• Not scaling defects per 1000 lines
• Confusing defects per line with per 1000 lines
• Incorrect division order

4. A defect report shows severity as 'High' but the status is marked 'Closed' without fixing. What is the likely issue?

medium

A. Defect density calculation error

B. Defect was fixed but not retested

C. Test cases are incomplete

D. Defect severity and status are inconsistent

Solution

1. Step 1: Analyze severity and status meaning

   High severity means serious defect; status 'Closed' usually means fixed or resolved.

2. Step 2: Identify inconsistency

   Closing a high severity defect without fixing is inconsistent and indicates a reporting or process error.

3. Final Answer:

   Defect severity and status are inconsistent -> Option D

4. Quick Check:

   High severity + Closed without fix = inconsistency [OK]

Hint: High severity defects should not be closed without fix [OK]

Common Mistakes:

• Assuming defect density relates to status
• Confusing test case completeness with defect status
• Thinking defect was fixed without evidence

5. You have two modules: Module A with 2000 lines and 40 defects, Module B with 1000 lines and 30 defects. Which module has better quality based on defect density?

hard

A. Module A has better quality because it has fewer defects

B. Module A has better quality because its defect density is lower

C. Both modules have the same quality

D. Module B has better quality because it has fewer lines of code

Solution

1. Step 1: Calculate defect density for Module A

   Module A defect density = 40 / 2000 = 0.02 defects per line.

2. Step 2: Calculate defect density for Module B

   Module B defect density = 30 / 1000 = 0.03 defects per line.

3. Step 3: Compare defect densities

   Lower defect density means better quality; Module A's 0.02 is less than Module B's 0.03.

4. Final Answer:

   Module A has better quality because its defect density is lower -> Option B

5. Quick Check:

   0.02 < 0.03 means Module A better [OK]

Hint: Lower defect density means better quality [OK]

Common Mistakes:

• Comparing only defect counts without size
• Assuming fewer lines means better quality
• Saying both have same quality without calculation