Artificial Intelligence: How GPT is Changing the Approach to Software Development

Automation of routine tasks

One of the main problems of development is the huge amount of routine work: writing boilerplate code, documentation, test generation. GPT is able to take on a significant part of this load.

Example: Generating boilerplate code

Imagine a developer who creates a REST API in Python using FastAPI. Instead of manually writing each endpoint, the developer describes their idea in natural language:

"Create an API for task management: add a task, get a list of tasks, update a task by ID, and delete a task."

GPT generates ready-made code:

from fastapi import FastAPI

app = FastAPI()

tasks = {}

@app.post("/tasks/")
def create_task(task_id: int, description: str):
    tasks[task_id] = description
    return {"message": "Task created"}

@app.get("/tasks/")
def get_tasks():
    return tasks

@app.put("/tasks/{task_id}")
def update_task(task_id: int, description: str):
    tasks[task_id] = description
    return {"message": "Task updated"}

@app.delete("/tasks/{task_id}")
def delete_task(task_id: int):
    tasks.pop(task_id, None)
    return {"message": "Task deleted"}

This approach allows you to reduce the time spent on developing standard solutions.

Improving code quality

Another important task is to maintain the cleanliness and readability of the code. GPT can serve as your personal code reviewer, pointing out potential problems and suggesting improvements.

Example: Code refactoring

The developer passes a fragment of poor-quality code to GPT:

def process_data(data):
    for i in range(len(data)):
        if data[i] % 2 == 0:
            data[i] = data[i] * 2
        else:
            data[i] = data[i] + 1
    return data

GPT suggests an improvement:

def process_data(data):
    return [x * 2 if x % 2 == 0 else x + 1 for x in data]

Code reduction makes it more readable and maintainable.

Accelerating the training of new developers

GPT also becomes a powerful tool for training beginners. The model can explain complex concepts in simple terms, generate code examples, and even develop training assignments.

Example: Explaining a complex topic

A young developer wants to understand how recursion works. Instead of searching for information in dozens of sources, he asks GPT the question: "Explain what recursion is, with an example in Python." GPT responds:

Recursion is a function that calls itself. It is useful for tasks that can be broken down into identical sub-tasks. For example, calculating the factorial of a number:

def factorial(n):
    if n == 0:
        return 1
    return n * factorial(n - 1)

print(factorial(5))  # Output: 120

GPT helps not only to understand the concept but also to try it out in practice.

Testing and debugging

Testing is a key part of development, and GPT can significantly simplify this process. The model can generate test cases, find errors in the code, and even help write unit tests.

Example: Generating tests

The developer provides GPT with a function:

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

GPT generates tests:

import unittest

class TestAddFunction(unittest.TestCase):
    def test_add_positive_numbers(self):
        self.assertEqual(add(2, 3), 5)

    def test_add_negative_numbers(self):
        self.assertEqual(add(-2, -3), -5)

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

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

With this approach, you can quickly cover the code with tests.

Generating ideas and prototypes

GPT helps developers come up with ideas and create quick prototypes. For example, when designing interfaces or new features.

Example: Prototyping

The team is working on a financial management application and wants to add expense forecasting. The developer describes the task: "Forecast user expenses based on their previous data." GPT generates a prototype algorithm:

import numpy as np
from sklearn.linear_model import LinearRegression

def predict_expenses(expenses):
    x = np.arange(len(expenses)).reshape(-1, 1)
    y = np.array(expenses)
    model = LinearRegression().fit(x, y)
    future = np.array([len(expenses) + i for i in range(1, 6)])
    return model.predict(future)

expenses = [100, 150, 200, 250, 300]
print(predict_expenses(expenses))

Developers can quickly test an idea and refine it.

Ethics and Challenges

Using GPT in development raises important questions: who is responsible for errors generated by AI? How to avoid creating poor quality or unsafe code? It is important to understand that GPT is just a tool, and the responsibility for the final result always lies with the developer.

Conclusion

GPT is already changing the approach to software development, making the process faster, better, and more convenient. From automating routine tasks to helping with learning and testing, the potential for using language models in development is enormous. However, as with any powerful tool, it is important to use it wisely.

If you haven't tried using GPT in your work yet, now is the time to start. The future is already here, and it can be integrated into your code!