Equivalence partitioning and boundary value analysis in Software Engineering - Mini Project: Build & Apply

Equivalence Partitioning and Boundary Value Analysis

📖 Scenario: You are a QA engineer designing test cases for a function that validates a student's exam score. The score must be an integer between 0 and 100 (inclusive). You will use equivalence partitioning and boundary value analysis to derive a minimal but effective test set.

🎯 Goal: Build a Python model that defines equivalence partitions, selects representative values, identifies boundary values, and combines them into a final test set with expected results.

📋 What You'll Learn

Define equivalence partitions for the score input domain

Select one representative value from each partition

Identify boundary values at partition edges

Combine partitions and boundaries into a final test set with expected outcomes

💡 Why This Matters

🌍 Real World

EP and BVA are used in every QA department to design efficient test cases. They are standard techniques in ISTQB certification and required knowledge for QA roles.

💼 Career

QA engineers and SDETs are expected to derive test cases from specifications using EP and BVA. These techniques appear in virtually every software testing interview.

1

Define equivalence partitions

Create a dictionary called partitions with three entries: 'invalid_low' mapped to 'score < 0', 'valid' mapped to '0 <= score <= 100', and 'invalid_high' mapped to 'score > 100'.

Software Engineering

# Define the equivalence partitions for score 0-100
# Your code here

Hint

Three partitions cover all possible integer inputs: below range, within range, and above range.

2

Select representative values from each partition

Create a dictionary called partition_tests with three entries: 'invalid_low' mapped to -10, 'valid' mapped to 50, and 'invalid_high' mapped to 150.

Software Engineering

partitions = {
    'invalid_low': 'score  100'
}
# Select one representative value from each partition
# Your code here

Hint

Pick any value well inside each partition. The exact value does not matter as long as it clearly belongs to that class.

3

Identify boundary values

Create a dictionary called boundary_tests with six entries: 'below_lower' mapped to -1, 'at_lower' mapped to 0, 'above_lower' mapped to 1, 'below_upper' mapped to 99, 'at_upper' mapped to 100, and 'above_upper' mapped to 101.

Software Engineering

partitions = {
    'invalid_low': 'score  100'
}
partition_tests = {
    'invalid_low': -10,
    'valid': 50,
    'invalid_high': 150
}
# Identify boundary values at partition edges
# Your code here

Hint

Test one value below, at, and above each boundary. For lower boundary 0: test -1, 0, 1. For upper boundary 100: test 99, 100, 101.

4

Combine into final test set with expected outcomes

Create a dictionary called final_tests. Use a for loop with variables name and value over partition_tests.items(), setting final_tests[value] to 'valid' if 0 <= value <= 100 else 'invalid'. Then use another for loop with name and value over boundary_tests.items() doing the same.

Software Engineering

partitions = {
    'invalid_low': 'score  100'
}
partition_tests = {
    'invalid_low': -10,
    'valid': 50,
    'invalid_high': 150
}
boundary_tests = {
    'below_lower': -1,
    'at_lower': 0,
    'above_lower': 1,
    'below_upper': 99,
    'at_upper': 100,
    'above_upper': 101
}
# Combine all tests with expected outcomes
# Your code here

Hint

Each test value gets an expected outcome: valid if within 0-100, invalid otherwise.