Can AI Models Generate Working Code?

• 26/5/2024
• 4-minute read

Can AI Language Models Write Working Code?

Large language models (LLMs) have revolutionized many fields, from content creation to data analysis. But one question particularly interests developers: can these AI systems write code that actually works? This article explores LLMs’ code generation capabilities, limitations, and real-world applications.

The Promise of AI-Generated Code

Modern LLMs can generate code in various programming languages by learning patterns from vast datasets of publicly available code repositories. Given the right prompt, these models can produce syntactically correct code that often mirrors human-written solutions.

The potential benefits are significant:

• Time savings on repetitive coding tasks
• Reduced barrier to entry for programming beginners
• Quick prototyping capabilities for experienced developers

Compilable vs. Correct: The Important Distinction

A key difference exists between code that compiles and code that functions correctly. LLMs excel at producing syntactically valid code but may struggle with:

1. Logical errors that only appear at runtime
2. Edge cases not explicitly mentioned in prompts
3. Security vulnerabilities and best practices
4. Performance optimizations

Real-World Examples: LLMs in Action

Let’s examine how modern LLMs perform on real coding tasks:

Example 1: Simple Function in Python

When asked to “Write a function to check if a string is a palindrome,” an LLM might produce:

def is_palindrome(text):
    """
    Check if the given string is a palindrome.
    A palindrome reads the same forward and backward.
    
    Args:
        text (str): The string to check
        
    Returns:
        bool: True if the string is a palindrome, False otherwise
    """
    # Remove spaces and convert to lowercase
    clean_text = ''.join(text.lower().split())
    # Compare with its reverse
    return clean_text == clean_text[::-1]

# Example usage
print(is_palindrome("radar"))  # True
print(is_palindrome("hello"))  # False
print(is_palindrome("A man a plan a canal Panama"))  # True

This function correctly implements palindrome checking with proper documentation and examples.

Example 2: Java Class Implementation

For a more complex task like “Create a Java class for a bank account with deposit and withdrawal methods”:

public class BankAccount {
    private String accountNumber;
    private String accountHolder;
    private double balance;
    
    public BankAccount(String accountNumber, String accountHolder, double initialBalance) {
        this.accountNumber = accountNumber;
        this.accountHolder = accountHolder;
        this.balance = initialBalance;
    }
    
    public double deposit(double amount) {
        if (amount <= 0) {
            throw new IllegalArgumentException("Deposit amount must be positive");
        }
        balance += amount;
        return balance;
    }
    
    public double withdraw(double amount) {
        if (amount <= 0) {
            throw new IllegalArgumentException("Withdrawal amount must be positive");
        }
        if (amount > balance) {
            throw new IllegalArgumentException("Insufficient funds");
        }
        balance -= amount;
        return balance;
    }
    
    public double getBalance() {
        return balance;
    }
    
    // Getters and additional methods omitted for brevity
}

While this code compiles correctly, a human developer would still need to review it for business logic correctness and edge cases.

Leading AI Code Generation Models

Recent advancements in AI-powered coding tools have produced several impressive models:

Where LLMs Succeed and Fail

Strengths:

• Generating boilerplate code and common patterns
• Suggesting solutions for well-defined problems
• Implementing standard algorithms and data structures
• Converting between programming languages

Limitations:

• Maintaining consistency across large codebases
• Understanding complex business logic
• Debugging non-trivial runtime issues
• Adhering to project-specific conventions

Best Practices for Working with AI-Generated Code

To maximize the benefits while minimizing risks:

1. Verify functionality through comprehensive testing
2. Review for security issues before incorporating into production
3. Understand the code rather than blindly accepting suggestions
4. Use AI for rapid prototyping, then refine manually
5. Combine AI assistance with human expertise

The Future of AI in Software Development

As LLMs continue to evolve, they’re likely to become indispensable programming assistants rather than replacements for human developers. The most effective approach will be collaborative: humans providing domain knowledge and creative problem-solving, with AI handling implementation details and routine tasks.

Research indicates that developers using AI coding assistants can complete tasks up to 55% faster while maintaining code quality. This productivity boost allows developers to focus on more creative and strategic aspects of software development.

Conclusion

While LLMs can indeed write compilable code, the answer to “can they write working code?” is more nuanced. For straightforward tasks, LLMs perform remarkably well. For complex systems, human oversight remains essential.

The future of software development likely involves a symbiotic relationship between human creativity and AI assistance—each playing to their strengths to create better software more efficiently than either could alone.

Further Reading:

• GitHub Copilot’s Impact on Developer Productivity
• How AI Is Changing Software Development
• IEEE Spectrum: The State of AI Programming
• MIT Technology Review: AI Code Generation