Testing and Debugging

Duration: 8 min

This module delves into the critical aspects of testing and debugging in Python, essential for ensuring the reliability and efficiency of AI applications. Mastery of these skills allows developers to identify and rectify errors early, maintain code quality, and optimize performance.

Understanding Unit Tests

Unit testing is a method by which individual units of source code, sets of one or more computer program modules together with associated control data, usage procedures, and operating procedures, are tested to determine if they are fit for use. It involves decomposing a program into the smallest manageable sections of code called units and then testing those units in isolation from the rest of the program.

example1.py

import unittest

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

class TestMathOperations(unittest.TestCase):

    def test_add(self):
        self.assertEqual(add(1, 2), 3)
        self.assertEqual(add(-1, 1), 0)
        self.assertEqual(add(-1, -1), -2)

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

Try it in Google Colab:

...
 Ran 1 test from 1 test case
 Ran 3 subtests from 1 test case
 Ran 1 test in 0.001s

OK

Debugging Techniques

Debugging is the process of identifying and resolving bugs or defects within a computer program that prevent correct operation. Python provides several tools and techniques for debugging, including the use of the built-in pdb module, which allows for setting breakpoints, stepping through code, and inspecting variables at runtime.

example2.py

import pdb

def divide(a, b):
    try:
        return a / b
    except ZeroDivisionError as e:
        pdb.post_mortem()

divide(10, 0)

💡 Tip: When using pdb, remember to use commands like 'n' to execute the next line,'s' to step into a function, and 'q' to quit debugging.