Lesson 19: Testing & Debugging

Lesson 19: Testing & Debugging#

Owner: Rushikesh Shirsat

Introduction to Software Testing#

1.1 What is Software Testing?#

Software Testing is a process of verifying that a software application works as expected
and meets the user’s requirements. It helps identify bugs, errors, and performance issues
before the software is released.

Why Testing is Important#

  • Ensures quality and reliability of software.

  • Detects defects early, saving time and cost.

  • Improves security, performance, and user satisfaction.

  • Prevents system failures after deployment.

1.2 Key Testing Terminologies#

Term

Meaning

Bug/Defect

A flaw that causes software to behave unexpectedly.

Error

A human mistake that introduces a defect.

Test Case

A set of conditions or inputs to check a specific feature.

Test Plan

A document that outlines scope, approach, and schedule of testing.

Expected Output

The correct result we anticipate.

Actual Output

The real result produced during the test.

1.3 Goals of Software Testing#

  • Verification: Are we building the product right?

  • Validation: Are we building the right product?

  • Defect Detection: Finding issues before deployment.

  • Quality Assurance: Ensuring the product meets standards.

2 Levels of Testing#

Level

Focus

Example

Unit Testing

Testing individual functions or modules.

Function add(a, b)

Integration Testing

Testing the interaction between modules.

user_login() calling db_connect()

System Testing

Testing the whole application end-to-end.

Full web app test

Acceptance Testing

Validating with client requirements.

UAT before deployment

2.1 Unit Testing#

Goal: Verify that individual parts (functions, methods) work as expected.
Tool: unittest or pytest

Example:#

# math_utils.py
def multiply(a, b):
    return a * b

Test:#

# test_math_utils.py
import unittest
from math_utils import multiply

class TestMathUtils(unittest.TestCase):
    def test_multiply(self):
        self.assertEqual(multiply(3, 4), 12)
        self.assertNotEqual(multiply(2, 2), 5)

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

Output:

.
----------------------------------------------------------------------
Ran 1 test in 0.000s
OK

2.2 Integration Testing#

Goal: Verify that different modules work together correctly.

# db_module.py
def connect_db():
    return True

# user_module.py
from db_module import connect_db

def login_user(username, password):
    if connect_db() and username == "admin" and password == "1234":
        return "Login Success"
    else:
        return "Login Failed"

Test:#

import unittest
from user_module import login_user

class TestIntegration(unittest.TestCase):
    def test_login(self):
        result = login_user("admin", "1234")
        self.assertEqual(result, "Login Success")

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

2.3 System Testing#

Goal: Validate the entire system as a whole (end-to-end functionality). Often done using automation tools (e.g., Selenium for web apps).

Example:

import unittest
from user_module import login_user

class TestIntegration(unittest.TestCase):
    def test_login(self):
        result = login_user("admin", "1234")
        self.assertEqual(result, "Login Success")

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

Output:

System test passed!

2.4 Acceptance Testing#

Goal: Confirm the system meets the client’s or stakeholder’s requirements.

Example:

def add(a, b):
    return a + b

def acceptance_test():
    assert add(2, 3) == 5, "Requirement not met"
    print("Acceptance Test Passed!")

acceptance_test()

Output:

Acceptance Test Passed!

There are two broad categories:

  • Functional Testing

  • Non-functional Testing

3.1 Functional Testing#

Examples:

  • Unit Testing

  • Integration Testing

  • Regression Testing

  • User Acceptance Testing

  • Smoke Testing

Example: Regression Testing#

def add(a, b):
    return a + b + 0  # minor refactor

def test_regression():
    assert add(2, 3) == 5

test_regression()
print("Regression test passed!")

NOTE: Ensures new changes didn’t break old features.

3.2 Non-Functional Testing#

Examples:

  • Performance Testing

  • Load Testing

  • Stress Testing

  • Security Testing

  • Usability Testing

Example (Performance check):#

import time

def big_task():
    time.sleep(1)
    return "Done"

start = time.time()
result = big_task()
end = time.time()

assert (end - start) < 2
print("Performance Test Passed!")

3.3 Exploratory and Ad-hoc Testing#

  • Exploratory: Explore the system freely.

  • Ad-hoc: Informal testing to discover issues quickly.

4.1 Why Automate?#

  • Saves time for repetitive tests.

  • Improves accuracy.

  • Enables CI/CD integration.

  • Faster feedback for developers.

4.2 Automated Testing Tools#

Tool

Language

Use Case

unittest

Python

Built-in testing

pytest

Python

Simple test writing

Selenium

Python/Java

Web UI automation

Jenkins

Any

CI server

4.3 Example: Pytest#

# calculator.py
def add(a, b):
    return a + b

# test_calculator.py
from calculator import add

def test_add():
    assert add(2, 3) == 5
    assert add(-1, 1) == 0

Run Command:

pytest test_calculator.py

Output:

============================== test session starts ==============================
collected 1 item
test_calculator.py . [100%]
=============================== 1 passed in 0.01s ===============================

5.1 Test Case Template#

Field

Description

Test ID

Unique identifier

Test Description

What the test is validating

Input Data

Test inputs

Expected Result

What should happen

Actual Result

What happened

Status

Pass/Fail

5.2 Example#

Test ID

Description

Input

Expected Output

Actual Output

Result

TC_01

Add positive numbers

(2,3)

5

5

Pass

TC_02

Add negative numbers

(-1,-1)

-2

-2

Pass

  1. What is the main goal of software testing?

  2. Define “defect” and “test case.”

  3. Why is expected vs actual output important?

  4. What could happen if we skip testing?

  5. What is regression?

MCQs#

  1. Software testing ensures: A. Performance only B. Code compilation only C. Software works as expected D. System design ➤ Answer: C

  2. A test case includes: A. Input and expected output B. Source code only C. Documentation only D. UI design ➤ Answer: A

  1. What is the focus of unit testing?

  2. What do we test during integration testing?

  3. Which testing level involves the whole system?

  4. Who performs acceptance testing?

  5. Why should testing be layered?

MCQs#

Unit testing verifies: A. Database connections B. Entire system behavior C. Individual functions or classes D. User interface ➤ Answer: C

Integration testing ensures: A. Code style B. Module interactions C. Syntax checking D. User experience ➤ Answer: B

  1. Differentiate functional and non-functional testing.

  2. What is regression testing used for?

  3. Give an example of performance testing.

  4. What is exploratory testing?

  5. What does load testing measure?

MCQs#

Non-functional testing includes: A. Integration Testing B. Unit Testing C. Load Testing D. Acceptance Testing ➤ Answer: C

Regression testing is done to: A. Find new bugs B. Ensure old features work after changes C. Optimize UI D. Speed up performance ➤ Answer: B

  1. Why use automation testing?

  2. Name two Python testing frameworks.

  3. What does pytest do when a test fails?

  4. What is the command to run pytest?

  5. Which type of testing is best automated?

MCQs#

Automation testing helps: A. Reduce human error B. Slow down testing C. Replace debugging D. Skip CI/CD ➤ Answer: A

Which tool is used for web testing? A. pytest B. Selenium C. unittest D. coverage ➤ Answer: B

  1. What is a test case?

  2. What information should it contain?

  3. What is the role of a test ID?

  4. What do you do if a test fails?

  5. Difference between expected and actual output?

Test cases should be: A. Random B. Repeatable and clear C. Unstructured D. Unplanned ➤ Answer: B

Learning Objectives#

  • Understand what debugging is and why it’s necessary.

  • Identify types of errors in code.

  • Use debugging methods: print, logging, IDE tools.

  • Handle exceptions using try-except.

  • Use Python’s debugger (pdb).

  • Apply systematic strategies to locate bugs.

1.1 What is Debugging?#

Debugging is the process of detecting, analyzing, and fixing bugs.

1.2 Common Types of Errors#

Error Type

Description

Example

Syntax Error

Violates Python grammar rules

print "Hello"

Runtime Error

Happens during execution

10 / 0

Logical Error

Program runs but produces incorrect output

wrong area formula

1.3 Debugging vs Testing#

Aspect

Testing

Debugging

Purpose

Detect presence of bugs

Identify & fix cause of bugs

Who performs

QA/Test Engineer

Developer

When

After implementation

During/after testing

Example#

def divide(a, b):
    return a / b

print(divide(10, 0))

Error:

ZeroDivisionError: division by zero

Debugging Process and Strategy#

2.1 Debugging Steps#

  1. Identify the bug

  2. Reproduce the bug

  3. Diagnose the cause

  4. Fix the issue

  5. Verify the fix

2.2 Example Process#

def total_marks(a, b, c):
    return a + b - c

print(total_marks(80, 90, 70))  # Expected 240, gets 100

Fix:

def total_marks(a, b, c):
    return a + b + c

print(total_marks(80, 90, 70))

Tool

Description

Print statements

Trace variables

Logging

Track program flow

IDE Debugger

Visual debugging tools

pdb

Built-in Python debugger

Assertions

Check assumptions

3.1 What is Print Debugging?#

Advantages:

  • Simple

  • No setup

Disadvantages:

  • Clutters code

  • Must be removed later

3.2 Example#

def average(nums):
    print("DEBUG: nums =", nums)
    total = sum(nums)
    print("DEBUG: total =", total)
    avg = total / len(nums)
    print("DEBUG: avg =", avg)
    return avg

print(average([2, 4, 6]))

Output:

DEBUG: nums = [2, 4, 6]
DEBUG: total = 12
DEBUG: avg = 4.0
4.0

4.1 What is Logging?#

Logging records events for debugging and monitoring.

4.2 Example:#

import logging
logging.basicConfig(level=logging.DEBUG)

def divide(a, b):
    logging.debug(f"Inputs: a={a}, b={b}")
    try:
        result = a / b
        logging.info(f"Result: {result}")
        return result
    except ZeroDivisionError:
        logging.error("Division by zero!")
        return None

divide(10, 2)
divide(10, 0)

Output:

DEBUG: Inputs: a=10, b=2
INFO: Result: 5.0
DEBUG: Inputs: a=10, b=0
ERROR: Division by zero!

What is pdb?#

A command-line debugger.

Example:#

def calc_sum(a, b):
    return a + b

import pdb
pdb.set_trace()

x = 5
y = 10
result = calc_sum(x, y)
print(result)

Commands:

Command

Action

n

next line

s

step into

c

continue

p var

print variable

q

quit

Exception Handling and Assertions#

6.1 Exception Handling#

try:
    x = int(input("Enter a number: "))
    print(10 / x)
except ZeroDivisionError:
    print("Cannot divide by zero.")
except ValueError:
    print("Invalid input. Please enter a number.")
finally:
    print("Execution completed.")

Using Assertions#

def square_root(x):
    assert x >= 0, "x must be non-negative"
    return x ** 0.5

print(square_root(9))
print(square_root(-1))

7.1 Tips#

  • Reproduce bugs

  • Isolate code

  • Use version control

  • Keep logs

  • Add assertions

  • Don’t ignore warnings

7.2 Example#

def calculate_discount(price, discount):
    final_price = price - discount * price
    return final_price

print(calculate_discount(100, 0.1))
print(calculate_discount(100, 10))

Fix:

def calculate_discount(price, discount_percent):
    if discount_percent > 1:
        discount_percent = discount_percent / 100
    return price - (discount_percent * price)

  1. What is the main goal of software testing?

  2. Define “defect” and “test case.”

  3. Why is expected vs actual output important?

  4. What could happen if we skip testing?

  5. What is regression?

MCQs#

  1. Software testing ensures:
    A. Performance only
    B. Code compilation only
    C. Software works as expected
    D. System design
    ➤ Answer: C
  2. A test case includes:
    A. Input and expected output
    B. Source code only
    C. Documentation only
    D. UI design
    ➤ Answer: A

  1. What is the focus of unit testing?

  2. What do we test during integration testing?

  3. Which testing level involves the whole system?

  4. Who performs acceptance testing?

  5. Why should testing be layered?

MCQs#

Unit testing verifies:
A. Database connections
B. Entire system behavior
C. Individual functions or classes
D. User interface
➤ Answer: C
Integration testing ensures:
A. Code style
B. Module interactions
C. Syntax checking
D. User experience
➤ Answer: B

  1. Differentiate functional and non-functional testing.

  2. What is regression testing used for?

  3. Give an example of performance testing.

  4. What is exploratory testing?

  5. What does load testing measure?

MCQs#

Non-functional testing includes:
A. Integration Testing
B. Unit Testing
C. Load Testing
D. Acceptance Testing
➤ Answer: C
Regression testing is done to:
A. Find new bugs
B. Ensure old features work after changes
C. Optimize UI
D. Speed up performance
➤ Answer: B

  1. Why use automation testing?

  2. Name two Python testing frameworks.

  3. What does pytest do when a test fails?

  4. What is the command to run pytest?

  5. Which type of testing is best automated?

MCQs#

Automation testing helps:
A. Reduce human error
B. Slow down testing
C. Replace debugging
D. Skip CI/CD
➤ Answer: A
Which tool is used for web testing?
A. pytest
B. Selenium
C. unittest
D. coverage
➤ Answer: B

  1. What is a test case?

  2. What information should it contain?

  3. What is the role of a test ID?

  4. What do you do if a test fails?

  5. Difference between expected and actual output?

MCQs#

Test cases should be:
A. Random
B. Repeatable and clear
C. Unstructured
D. Unplanned
➤ Answer: B

  1. List the 5 debugging steps.

  2. What tool can you use to trace program execution?

  3. Why should you reproduce a bug?

  4. What is the difference between print and logging?

  5. What is the purpose of assertions?

MCQs#

  1. The first step in debugging is:
    A. Fix the bug
    B. Verify the fix
    C. Identify and reproduce the bug
    D. Write test cases
    ➤ Answer: C
  2. Which of the following can be used for command-line debugging?
    A. pytest
    B. pdb
    C. unittest
    D. logging
    ➤ Answer: B
  3. finally blocks:
    A. Run only on error
    B. Always run
    C. Never run if an error occurs
    D. Execute only if no except block exists
    ➤ Answer: B
  4. AssertionError is raised when:
    A. An exception occurs
    B. A test case fails
    C. An assert condition evaluates to False
    D. Division by zero
    ➤ Answer: C
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