Data integrity checks in Testing Fundamentals - Test Execution Trace

Test Overview

This test verifies that data saved in a database remains accurate and unchanged after a save operation. It checks if the data retrieved matches the data input, ensuring data integrity.

Test Code - unittest

Testing Fundamentals

import unittest

class TestDataIntegrity(unittest.TestCase):
    def setUp(self):
        # Simulate a simple in-memory database as a dictionary
        self.database = {}

    def save_data(self, key, value):
        # Simulate saving data
        self.database[key] = value

    def get_data(self, key):
        # Simulate retrieving data
        return self.database.get(key, None)

    def test_data_integrity_after_save(self):
        input_data = {'id': 1, 'name': 'Alice', 'age': 30}
        self.save_data(input_data['id'], input_data)
        retrieved_data = self.get_data(input_data['id'])
        self.assertEqual(retrieved_data, input_data, "Data retrieved does not match data saved")

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

Execution Trace - 5 Steps

Step	Action	System State	Assertion	Result
1	Test starts and setUp method initializes an empty in-memory database	Empty dictionary simulating database: {}	—	PASS
2	Test calls save_data with key=1 and value={'id':1, 'name':'Alice', 'age':30}	Database now contains: {1: {'id':1, 'name':'Alice', 'age':30}}	—	PASS
3	Test calls get_data with key=1 to retrieve saved data	Retrieved data is {'id':1, 'name':'Alice', 'age':30}	Check if retrieved data equals input data	PASS
4	Assertion compares retrieved data with input data	Both data sets match exactly	assertEqual passes confirming data integrity	PASS
5	Test ends successfully	No errors, test passed	—	PASS

Failure Scenario

Failing Condition: Data retrieved from the database does not match the data saved (e.g., data corruption or incorrect save)

Execution Trace Quiz - 3 Questions

What does the test verify after saving data?

A That the data is deleted after saving

B That the database is empty

C That the data retrieved matches the data saved

D That the data is saved twice

Key Result

Always verify that data retrieved after saving matches exactly what was saved to ensure data integrity and catch any corruption or unintended changes.

Practice

1. What is the main purpose of data integrity checks in software testing?

easy

A. To ensure data remains accurate and unchanged during processing

B. To improve the speed of the software

C. To add new features to the software

D. To change data formats automatically

Solution

1. Step 1: Understand the meaning of data integrity

   Data integrity means data is accurate, consistent, and unchanged during operations.

2. Step 2: Identify the goal of data integrity checks

   These checks confirm that data stays correct and unaltered through software processes.

3. Final Answer:

   To ensure data remains accurate and unchanged during processing -> Option A

4. Quick Check:

   Data integrity = data accuracy and consistency [OK]

Hint: Data integrity means data stays correct and unchanged [OK]

Common Mistakes:

• Confusing data integrity with performance improvements
• Thinking data integrity changes data formats
• Assuming data integrity adds new features

2. Which of the following is the correct way to assert that two data values are equal in a test?

easy

A. assertEqual(expected, actual)

B. assertEquals(expected, actual)

C. assertEqual(actual, expected)

D. assertEqual(actual == expected)

Solution

1. Step 1: Recall the correct syntax for equality assertion

   The common syntax is assertEqual(expected, actual) to compare values.

2. Step 2: Check each option for syntax correctness

   assertEqual(expected, actual) matches the correct order and syntax; others have wrong order or wrong method name.

3. Final Answer:

   assertEqual(expected, actual) -> Option A

4. Quick Check:

   assertEqual(expected, actual) is correct [OK]

Hint: Remember: expected value first, actual second in assertEqual [OK]

Common Mistakes:

• Swapping expected and actual parameters
• Using wrong assertion method names
• Passing a boolean expression instead of two values

3. Given the following test code snippet:

expected_data = {'id': 101, 'name': 'Alice'}
actual_data = {'id': 101, 'name': 'Alice'}
assert expected_data == actual_data, 'Data mismatch!'

What will be the test result?

medium

A. Test fails with 'Data mismatch!' error

B. SyntaxError due to wrong assertion

C. Test passes with no error

D. Test passes but prints 'Data mismatch!'

Solution

1. Step 1: Compare expected and actual data dictionaries

   Both dictionaries have the same keys and values, so they are equal.

2. Step 2: Understand assertion behavior

   Since the condition is true, the assertion passes without raising an error.

3. Final Answer:

   Test passes with no error -> Option C

4. Quick Check:

   Equal data means assertion passes [OK]

Hint: Assert passes if expected equals actual data [OK]

Common Mistakes:

• Assuming assertion prints message on success
• Confusing assertion syntax with function calls
• Thinking assertion fails when data matches

4. Identify the error in this data integrity check code:

expected = {'user': 'bob', 'age': 30}
actual = {'user': 'bob', 'age': '30'}
assert expected == actual, 'Mismatch found!'

medium

A. No error, the test will pass

B. The age values have different data types causing assertion failure

C. The dictionary keys are different

D. The assertion syntax is incorrect

Solution

1. Step 1: Compare dictionary keys and values

   Keys are the same, but 'age' values differ: integer 30 vs string '30'.

2. Step 2: Understand assertion behavior with type differences

   Since values differ in type, the assertion fails and raises 'Mismatch found!'.

3. Final Answer:

   The age values have different data types causing assertion failure -> Option B

4. Quick Check:

   Different types cause assertion failure [OK]

Hint: Check data types carefully when comparing values [OK]

Common Mistakes:

• Ignoring type differences in values
• Assuming assertion syntax is wrong
• Thinking keys differ when they don't

5. You have a list of user records with possible duplicate IDs:

users = [
  {'id': 1, 'name': 'Anna'},
  {'id': 2, 'name': 'Ben'},
  {'id': 1, 'name': 'Anna'},
  {'id': 3, 'name': 'Cara'}
]

Which data integrity check best detects duplicate user IDs?

hard

A. Verify all user IDs are integers

B. Compare user names directly for duplicates

C. Sort users by name and check order

D. Check if length of user list equals length of set of user IDs

Solution

1. Step 1: Understand the problem of duplicate IDs

   Duplicates mean some IDs appear more than once in the list.

2. Step 2: Use set to find unique IDs and compare lengths

   Convert IDs to a set (unique values). If set length is less than list length, duplicates exist.

3. Final Answer:

   Check if length of user list equals length of set of user IDs -> Option D

4. Quick Check:

   Set length < list length means duplicates [OK]

Hint: Use set to find unique IDs and compare lengths [OK]

Common Mistakes:

• Checking names instead of IDs for duplicates
• Sorting without checking duplicates
• Only verifying data types, not duplicates