I used ChatGPT to write a program to parse numbers in three different bases and print them in those three bases.
In the following transcript everything after the A:'s was generated by the chatbot. I formatted the text for this Markdown file.
Is the programming job doomed to be replaced by AI bots?
No, I don't think programming jobs are doomed to be replaced by AI bots. While AI and automation are becoming more prevalent in the field of software development, programming is a creative and complex process that involves much more than just writing code.
Programmers often need to work closely with users and stakeholders to understand their needs and design software that meets those needs. They need to have strong problem-solving skills and be able to think creatively to come up with solutions to complex problems. They also need to be able to communicate their ideas clearly to others, both in writing and verbally.
While AI and automation can certainly help programmers be more efficient, they can't replace the creativity, problem-solving ability, and communication skills that are essential for success in this field. Furthermore, programming is a constantly evolving field, with new technologies and frameworks emerging all the time, which means that there will always be a need for skilled programmers who can adapt to these changes.
In short, while the role of AI in programming is likely to continue to grow, programming jobs are not likely to be replaced by AI bots anytime soon.
How can programmers make use of AI to do their jobs?
Programmers can make use of AI in several ways to do their jobs more effectively and efficiently:
Automated testing: AI can be used to automate software testing, which can help to detect bugs and other issues much faster than manual testing. This can save programmers a lot of time and effort, and can also help to ensure that software is more reliable and of higher quality.
Code generation: Some AI systems are capable of generating code based on high-level descriptions of what the code should do. This can be particularly useful for generating boilerplate code, or for automating repetitive tasks.
Natural language processing: AI can be used to help programmers better understand natural language, which can be especially useful for reading and interpreting documentation or working with non-technical stakeholders.
Debugging: AI can be used to help identify and fix bugs in code. For example, some AI systems are capable of automatically generating patches for code that has been identified as buggy.
Predictive analytics: AI can be used to analyze large data sets and make predictions about user behavior, performance, and other factors that can affect software development. This can help programmers make more informed decisions about how to design and optimize software.
Overall, the key to making effective use of AI in programming is to identify specific use cases where AI can help to improve productivity, quality, or other aspects of software development, and then to integrate AI tools and technologies into existing workflows in a way that makes sense for the particular context.
Next, I prompted ChatGPT to write a command line program for me, with unit tests.
I asked for the functions I wanted, and then the program that uses them. I wanted to use base 2, base 10, and base 16 numbers.
Next, I asked for unit tests. The unit test framework wanted to test a module, so I asked how to make the functions into a module. I didn't specify the three bases when I asked for unit tests, so it surprised me by generating more generic functions than before.
Write a function in Python to print a number in base 2, base 10, and base 16.
Here's a Python function that takes a number as input and prints it in base 2, base 10, and base 16:
def print_bases(num):
print("Base 2:", bin(num))
print("Base 10:", num)
print("Base 16:", hex(num))
Here's how you can use this function:
>>> print_bases(123)
Base 2: 0b1111011
Base 10: 123
Base 16: 0x7b
In this example, we passed the number 123 to the print_bases function, which printed its representation in binary (0b1111011), decimal (123), and hexadecimal (0x7b).
Write a function in Python to parse a number in base 2, base 10, and base 16.
Here's a Python function that takes a string representing a number in either base 2, base 10, or base 16, and returns the number in all three bases:
def parse_bases(num_str):
num = int(num_str, 0)
return (bin(num), num, hex(num))
Here's how you can use this function:
>>> parse_bases("0b1111011")
('0b1111011', 123, '0x7b')
>>> parse_bases("123")
('0b1111011', 123, '0x7b')
>>> parse_bases("0x7b")
('0b1111011', 123, '0x7b')
In this example, we passed strings representing the number 123 in binary (0b1111011), decimal (123), and hexadecimal (0x7b) to the parse_bases function, which returned a tuple containing the number in binary ('0b1111011'), decimal (123), and hexadecimal ('0x7b'). The int function is used to convert the input string to an integer, with the 0 argument specifying that the function should try to interpret the string as a number in any of the supported bases (i.e., base 2, 8, 10, or 16) based on its prefix (if any).
Write a python function to that uses the parse_bases and print_bases functions to process numbers from the command line.
Notice I didn't use correct grammar in the question and it still understood me.
I didn't really ask for the command line program either, it just generated that code because it sensed I needed it. I guess.
Sure! Here's an example Python function that takes a command line argument representing a number in any of the three bases, uses the parse_bases function to parse it, and then uses the print_bases function to print it in all three bases:
import sys
def process_number(num_str):
bases = parse_bases(num_str)
print("Number in different bases:")
print("Binary:", bases[0])
print("Decimal:", bases[1])
print("Hexadecimal:", bases[2])
if __name__ == "__main__":
if len(sys.argv) != 2:
print("Usage: python process_number.py <number>")
else:
process_number(sys.argv[1])
To use this function, save it in a file called process_number.py, open a terminal, navigate to the directory where the file is saved, and run the command:
python process_number.py <number>
where number is a string representing the number you want to process in any of the three bases.
Here's an example usage of the function:
python process_number.py 0b1111011
This will output:
Number in different bases:
Binary: 0b1111011
Decimal: 123
Hexadecimal: 0x7b
Note that the function first checks that the number of command line arguments is exactly 2 (the name of the script plus the number), and prints a usage message if it's not. If the number of arguments is correct, it passes the number string to the process_number function to process it.
Write unit tests for Pytest to test the parse_bases and print_bases functions
Notice below that the parse_bases function takes different parameters than it did before. It's a more general function because I didn't say which bases to parse.
Sure, here are some sample unit tests for the parse_bases and print_bases functions using Pytest:
import pytest
from your_module import parse_bases, print_bases
def test_parse_bases():
assert parse_bases('1010', 2) == 10
assert parse_bases('255', 10) == 255
assert parse_bases('FF', 16) == 255
assert parse_bases('10000', 2) == 16
assert parse_bases('7F', 16) == 127
def test_print_bases(capsys):
print_bases(10)
captured = capsys.readouterr()
assert captured.out == '10 (0b1010, 0xA)\n'
print_bases(255)
captured = capsys.readouterr()
assert captured.out == '255 (0b11111111, 0xFF)\n'
print_bases(16)
captured = capsys.readouterr()
assert captured.out == '16 (0b10000, 0x10)\n'
In this example, we first import the parse_bases and print_bases functions from the module your_module that contains the functions we want to test.
We then define two test functions, test_parse_bases and test_print_bases, that each contain a series of assertions to test the functionality of the corresponding function.
For the test_parse_bases function, we assert that the output of parse_bases matches the expected values for several different inputs with different bases.
For the test_print_bases function, we use Pytest's capsys fixture to capture the output of the print_bases function and compare it to the expected output using the assert statement.
Note that these are just sample tests, and there may be additional edge cases and scenarios that would need to be tested depending on the specifics of your implementation.
How do I make a Python module that contains the parse_bases and print_bases functions?
Notice below that the parse_bases function takes different parameters than it did before. It's a more general function because I didn't say which bases to parse.
To create a Python module that contains the parse_bases and print_bases functions, you can follow these steps:
Create a new file in your preferred text editor or IDE, and save it with a name that ends with .py. For example, you could name it my_module.py.
Write the code for the parse_bases and print_bases functions in the file, like this:
def parse_bases(string, base):
return int(string, base)
def print_bases(num):
print(f"{num} (0b{bin(num)[2:]}, 0x{hex(num)[2:].upper()})")
Save the file.
Now that you have created a Python module, you can import the functions from this module in other Python scripts by using the import statement followed by the name of your module, like this:
import my_module
num = my_module.parse_bases('1010', 2)
my_module.print_bases(num)
This code will import the parse_bases and print_bases functions from my_module.py and use them to parse the string '1010' as a binary number and print the result in both binary and hexadecimal format.
That's all for my experiment with ChatGPT. It took longer to format the transcript than it did to produce the code.
Hmm...