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

Practice - 5 Tasks

1fill in blank

easy

Complete the code to show why systematic testing helps cover all cases.

Testing Fundamentals

test_cases = ['input1', 'input2', 'input3']
for case in [1]:
    print(f"Testing {case}")

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

Arandom.sample(test_cases, 2)

Btest_cases

C[]

DNone

Attempts:

3 left

💡 Hint

Common Mistakes

Using a subset of test cases by mistake.

Using the full list test_cases ensures all cases are tested systematically.

2fill in blank

medium

Complete the code to check if all inputs are covered by the tests.

Testing Fundamentals

all_inputs = {'a', 'b', 'c', 'd'}
tested_inputs = {'a', 'b', 'c'}
coverage = tested_inputs [1] all_inputs
print(coverage)

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

A!=

B>=

C==

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using < or > which only check subset or superset.

Using == checks if tested inputs exactly match all inputs, ensuring full coverage.

3fill in blank

hard

Fix the error in the code that tries to generate all combinations for testing.

Testing Fundamentals

from itertools import product
inputs1 = ['x', 'y']
inputs2 = ['1', '2']
all_combinations = list(product(inputs1, [1]))
print(all_combinations)

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

Ainputs2

Binputs1

CNone

D['3', '4']

Attempts:

3 left

💡 Hint

Common Mistakes

Using the same list twice or a wrong list.

To get all pairs, use inputs2 as the second input list in product.

4fill in blank

hard

Fill both blanks to create a test that only includes inputs longer than 3 characters.

Testing Fundamentals

inputs = ['cat', 'lion', 'tiger', 'dog']
filtered_tests = [[1] for word in inputs if len(word) [2] 3]
print(filtered_tests)

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

Aword.upper()

Bword.lower()

C>

D<=

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong comparison operator or no transformation.

We select words with length greater than 3 and convert them to uppercase for testing.

5fill in blank

hard

Fill all three blanks to build a dictionary of test results for inputs with length > 2.

Testing Fundamentals

inputs = ['go', 'run', 'jump', 'sit']
results = { [1]: [2] for word in inputs if len(word) [3] 2 }
print(results)

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

Aword

Blen(word)

C>

D{

Attempts:

3 left

💡 Hint

Common Mistakes

Missing dictionary braces or wrong filter condition.

The dictionary comprehension starts with {, uses word as key, length as value, and filters words longer than 2.

Practice

1. Why do systematic testing techniques improve test coverage?

easy

A. They focus only on testing the user interface elements.

B. They reduce the number of tests to save time without missing bugs.

C. They randomly select test cases to find bugs quickly.

D. They organize tests to cover important cases like boundaries and input groups.

Solution

1. Step 1: Understand the purpose of systematic techniques

   Systematic techniques plan tests to cover different important scenarios, such as boundaries and input groups.

2. Step 2: Compare options with this purpose

   Only They organize tests to cover important cases like boundaries and input groups. describes organizing tests to cover important cases, which matches the purpose.

3. Final Answer:

   They organize tests to cover important cases like boundaries and input groups. -> Option D

4. Quick Check:

   Systematic testing = organized coverage [OK]

Hint: Systematic means planned and organized tests [OK]

Common Mistakes:

• Thinking fewer tests always mean better coverage
• Confusing random testing with systematic testing
• Believing UI testing covers all bugs

2. Which of the following is a correct example of a systematic testing technique?

easy

A. Skipping tests for rare input values.

B. Testing inputs randomly without any plan.

C. Testing only the first and last inputs of a range (boundary value analysis).

D. Testing only the most popular feature repeatedly.

Solution

1. Step 1: Identify systematic techniques

   Boundary value analysis is a known systematic technique focusing on edge cases.

2. Step 2: Evaluate options

   Testing only the first and last inputs of a range (boundary value analysis). matches a systematic technique; others describe random or incomplete testing.

3. Final Answer:

   Testing only the first and last inputs of a range (boundary value analysis). -> Option C

4. Quick Check:

   Boundary value analysis = systematic test [OK]

Hint: Look for planned test methods like boundary value analysis [OK]

Common Mistakes:

• Confusing random testing with systematic
• Ignoring edge cases in tests
• Assuming popular features cover all bugs

3. Consider this code snippet for testing input groups:

inputs = [1, 5, 10, 15, 20]
covered_groups = set()
for i in inputs:
    if i <= 5:
        covered_groups.add('low')
    elif i <= 15:
        covered_groups.add('medium')
    else:
        covered_groups.add('high')
print(sorted(covered_groups))

What will be the output?

medium

A. ['low', 'medium', 'high']

B. ['low', 'medium']

C. ['medium', 'high']

D. ['low', 'high']

Solution

1. Step 1: Analyze input classification

   Inputs 1 and 5 are 'low', 10 and 15 are 'medium', 20 is 'high'.

2. Step 2: Determine covered groups

   All three groups 'low', 'medium', and 'high' are added to covered_groups.

3. Final Answer:

   ['low', 'medium', 'high'] -> Option A

4. Quick Check:

   All input groups covered = ['low', 'medium', 'high'] [OK]

Hint: Check each input group coverage carefully [OK]

Common Mistakes:

• Missing the 'high' group for input 20
• Assuming 15 is 'high' instead of 'medium'
• Forgetting to sort the output

4. A tester wrote this code to check boundary values:

def test_boundary(x):
    if x > 0 and x < 10:
        return 'Inside'
    else:
        return 'Outside'

print(test_boundary(0))
print(test_boundary(10))

What is the problem with this test regarding boundary coverage?

medium

A. It misses testing the exact boundary values 0 and 10 correctly.

B. It tests values outside the range only.

C. It uses incorrect syntax for the function.

D. It returns wrong strings for inputs.

Solution

1. Step 1: Understand boundary conditions in code

   The code checks if x is strictly greater than 0 and less than 10, so 0 and 10 are excluded.

2. Step 2: Identify test coverage issue

   Testing 0 and 10 returns 'Outside', missing the boundary values as 'Inside' cases.

3. Final Answer:

   It misses testing the exact boundary values 0 and 10 correctly. -> Option A

4. Quick Check:

   Boundary values excluded = coverage gap [OK]

Hint: Check if boundaries are included or excluded in conditions [OK]

Common Mistakes:

• Assuming 0 and 10 are inside the range
• Ignoring strict inequality signs
• Thinking syntax error causes the problem

5. You want to improve test coverage for a function that accepts numbers from 1 to 100. Which systematic technique combination will best improve coverage?

hard

A. Test only random numbers within 1 to 100 repeatedly.

B. Use boundary value analysis for edges and equivalence partitioning for input groups.

C. Test only the middle value 50 multiple times.

D. Test only numbers divisible by 10.

Solution

1. Step 1: Identify coverage goals

   We want to cover edge cases and different input groups systematically.

2. Step 2: Match techniques to goals

   Boundary value analysis covers edges (1 and 100), equivalence partitioning divides inputs into groups.

3. Step 3: Evaluate options

   Use boundary value analysis for edges and equivalence partitioning for input groups. combines both techniques, improving coverage best; others are incomplete or random.

4. Final Answer:

   Use boundary value analysis for edges and equivalence partitioning for input groups. -> Option B

5. Quick Check:

   Combine boundaries + partitions = best coverage [OK]

Hint: Combine boundary and group tests for full coverage [OK]

Common Mistakes:

• Relying on random or single-value tests
• Ignoring input group diversity
• Testing only multiples or arbitrary subsets