Autor: Codenga 12.06.2024

If you're at all interested in programming, you've likely heard of the ChatGPT tool. Thanks to its text analysis capabilities, we have the potential to generate and analyze code. In this article, we'll show you five interesting uses of ChatGPT in the programming process.

Remember, the code generated by ChatGPT will not always be perfect. Sometimes the results will fit our expectations perfectly, and sometimes they will require manual intervention.

Always try to verify the results returned by ChatGPT. Do not use code generated by this tool without checking if it meets your expectations.

1# Code Generation

One of the basic uses is generating code from scratch, based on the assumptions we provide. This can be very useful, for example, for generating a function.

Example prompt:

Write a Python function that sorts a list of numbers in ascending order.

ChatGPT's response:

def sort_list(list):
return sorted(list)

Sometimes you will need to create a more detailed prompt for ChatGPT to understand what code to generate. Don't hesitate to specify the expected result of the function, the parameters it should accept, etc.

2# Debugging

Another use is finding errors in the code. ChatGPT can help you analyze the code for errors and suggest fixes.

Example prompt:

Here is my Python code. I'm trying to add a number to a string, which causes an error. How can I fix it?

Here is the code we send to ChatGPT:

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

result = add_numbers(10, '20')
And here is the result, i.e., the code corrected by ChatGPT:

python
Copy code
def add_numbers(a, b):
    return a + int(b)

result = add_numbers(10, '20')

3# Writing Unit Tests

Testing is an important part of the software development process. However, there's no denying that writing unit tests can be tedious. ChatGPT can be very helpful here. It handles such monotonous tasks quite well, allowing us to focus on more creative things.

Example prompt:

Write unit tests in Python for a function that multiplies two numbers.

ChatGPT's response:

def multiply(a, b):
    return a * b

# Unit test
import unittest

class TestMathOperations(unittest.TestCase):
    def test_multiply(self):
        self.assertEqual(multiply(2, 3), 6)
        self.assertEqual(multiply(-1, 5), -5)
        self.assertEqual(multiply(0, 100), 0)

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

4# Writing Documentation

Describing and documenting code is another important task that has the potential for automation with ChatGPT. Let's try to create an appropriate prompt:

Write documentation for a Python function that divides two numbers and returns the result.

ChatGPT's response:

def divide(a, b):
"""
Divides two numbers.

:param a: Numerator
:param b: Denominator
:return: Result of dividing a by b
:raises ValueError: When the denominator is zero
"""
if b == 0:
    raise ValueError("Denominator cannot be zero")
return a / b

5# Code Refactoring

You probably know what code refactoring is. It's a process aimed at improving existing code, optimizing it, improving its readability, etc.

Example prompt:

Rewrite the following Python code to be more organized and consistent with object-oriented programming principles.

Here is the code sent to ChatGPT:

# Before refactoring
def calculate_area(shape, width, height):
    if shape == 'rectangle':
        return width * height
    elif shape == 'triangle':
        return 0.5 * width * height
    else:
        return None
    

And here is ChatGPT's response:

from abc import ABC, abstractmethod

class Shape(ABC):
    @abstractmethod
    def area(self):
        pass
    
class Rectangle(Shape):
    def __init__(self, width, height):
        self.width = width
        self.height = height
    
    def area(self):
        return self.width * self.height
    
class Triangle(Shape):
    def __init__(self, width, height):
        self.width = width
        self.height = height
    
    def area(self):
        return 0.5 * self.width * self.height
    
    def calculate_area(shape: Shape):
        return shape.area()
    

As you can see, ChatGPT handled this task quite well. Of course, you must always remember the necessity of controlling the results returned by this tool.

Summary

When used appropriately, ChatGPT can be an invaluable aid in programming. Its strength lies in the ability to write tedious, repetitive fragments such as unit tests or documentation. Try using the examples from the article as inspiration for your own explorations and experiments.