Why systematic techniques improve coverage in Testing Fundamentals - Test Execution Impact

Test Overview

This test simulates applying a systematic testing technique to ensure all parts of a simple calculator app are tested. It verifies that each function (add, subtract, multiply, divide) is covered by at least one test case, improving overall test coverage.

Test Code - unittest

Testing Fundamentals

import unittest

class Calculator:
    def add(self, a, b):
        return a + b
    def subtract(self, a, b):
        return a - b
    def multiply(self, a, b):
        return a * b
    def divide(self, a, b):
        if b == 0:
            raise ValueError("Cannot divide by zero")
        return a / b

class TestCalculator(unittest.TestCase):
    def setUp(self):
        self.calc = Calculator()

    def test_add(self):
        self.assertEqual(self.calc.add(2, 3), 5)

    def test_subtract(self):
        self.assertEqual(self.calc.subtract(5, 3), 2)

    def test_multiply(self):
        self.assertEqual(self.calc.multiply(4, 3), 12)

    def test_divide(self):
        self.assertEqual(self.calc.divide(10, 2), 5)

if __name__ == '__main__':
    unittest.main()

Execution Trace - 6 Steps

Step	Action	System State	Assertion	Result
1	Test starts	Test runner initializes the Calculator and TestCalculator classes	—	PASS
2	Runs test_add method	Calculator instance ready, inputs 2 and 3	Check if add(2, 3) == 5	PASS
3	Runs test_subtract method	Calculator instance ready, inputs 5 and 3	Check if subtract(5, 3) == 2	PASS
4	Runs test_multiply method	Calculator instance ready, inputs 4 and 3	Check if multiply(4, 3) == 12	PASS
5	Runs test_divide method	Calculator instance ready, inputs 10 and 2	Check if divide(10, 2) == 5	PASS
6	Test ends with all methods covered	All calculator functions tested once	Verify all functions have test coverage	PASS

Failure Scenario

Failing Condition: If any calculator function is not tested, e.g., test_divide method missing

Execution Trace Quiz - 3 Questions

What does the test_add method verify?

A That subtracting 2 from 3 returns 1

B That adding 2 and 3 returns 5

C That multiplying 2 and 3 returns 6

D That dividing 2 by 3 returns 0.66

Key Result

Using systematic techniques like writing separate tests for each function ensures all parts of the software are checked, improving test coverage and reducing the chance of missing bugs.

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