What are the best use cases for AI-generated code?

The past few years have seen a broad adoption of LLMs and AI tools, which has helped many industries, including software development. AI tools can now accomplish tasks in hours that previously took days or weeks.

For example, every developer is now using an AI code generation tool to improve their efficiency. These tools can predict and complete the next lines with a single button press. However, we can’t use these tools for everything since they can also negatively impact cognitive capabilities. So, what are the best use cases for AI-generated code?

How Does Code Generation Using AI Work?

AI-based code generation leverages deep learning techniques, primarily transformer-based models such as OpenAI’s Codex. These models analyze existing code patterns, natural language descriptions, and best practices to generate relevant code snippets. The process generally follows these steps:

• Input processing: The developer provides a prompt, such as a function signature, comment, or partial code.
• Model analysis: The AI model analyzes the input and predicts the best code completion or generation based on training data.
• Code output: The model generates code, which the developer can further refine.
• Validation and refinement: Developers test and optimize the generated code for accuracy and efficiency.

Best Use Cases for AI Code Generation

1. Boilerplate Code Generation

Developers often spend valuable time writing repetitive boilerplate code like:

• Project configurations
• CRUD operations
• Routing
• Setup files

AI-based tools like Qodo Gen, GitHub Copilot, and OpenAI’s Codex can automatically generate this scaffolding based on minimal input or context. This minimizes human error, accelerates development, and permits developers to direct their efforts toward business logic.

2. Automated Unit Test Generation

Writing unit tests is essential for ensuring software reliability. But it can be repetitive and time-consuming. AI tools can automatically generate unit tests by,

• Analyzing existing code
• Identifying functions
• Creating test cases based on typical input/output behavior

This accelerates test coverage, reduces human error, and frees up developers to focus on core logic and edge-case testing.

3. Code Refactoring and Optimization

AI can analyze code and suggest optimizations for performance, readability, and maintainability. This is especially useful for legacy codebases that require modernization.

Before:

def square_numbers(numbers):
    result = []
    for num in numbers:
        result.append(num * num)
    return result

After AI Optimization:

def square_numbers(numbers):
    return [num * num for num in numbers]

4. Converting Code Between Languages

Sometimes, developers have to move projects from one language to another. AI tools can ease and speed up the manual work involved in such conversions. For instance, a human would require a couple of minutes to change the following Python code to JavaScript. AI could do the same in a few seconds.

// Python

def average_above_freezing(temps):
    above_freezing = [t for t in temps if t > 0]
    if not above_freezing:
        return None
    return sum(above_freezing) / len(above_freezing)

// JavaScript

function averageAboveFreezing(temps) {
  const aboveFreezing = temps.filter(t => t > 0);
  if (aboveFreezing.length === 0) return null;
  const sum = aboveFreezing.reduce((a, b) => a + b, 0);
  return sum / aboveFreezing.length;
}

5. Database Query Generation

Writing complex SQL queries can be error-prone, especially when dealing with large schemas or intricate joins. AI-powered tools can translate natural language requests into optimized SQL queries. This helps both technical and non-technical users interact with databases more efficiently. This not only saves time but also reduces the learning curve for working with relational data.

6. Automating API Integration

Integrating third-party APIs often requires writing boilerplate code to handle requests, authentication, and error handling. AI tools can automate this process by generating client code from API documentation (like OpenAPI or Postman collections).

7. AI-Assisted Documentation Generation

Well-documented code is essential for maintainability, collaboration, and onboarding. However, writing documentation is often overlooked or delayed. AI tools can automatically generate meaningful docstrings, function descriptions, and API documentation by analyzing code structure, variable names, and logic.

def calculate_area(radius):
    """
    Calculate the area of a circle.
   
    Parameters:
        radius (float): The radius of the circle.
   
    Returns:
        float: The area of the circle.
    """
    return 3.14 * radius * radius

Conclusion

Everything from writing complex code to merging, translating, or even verifying outcomes, AI is going to take over the globe and is proving to be an extremely handy tool for developers. AI-based code generation is transforming software development by removing repetitive tasks while improving the quality of output and productivity.

By leveraging AI for code generation, developers can focus on creativity, problem-solving, and innovation while reducing the time spent on routine coding tasks.