Comparing Rule-Based and AI Methods for Code Conversion – Part 2

Code Translation Using Generative AI

Code translation using artificial intelligence (AI) is an innovative approach that significantly simplifies the process of converting program code from one language to another. Generative AI models, such as GPT (Generative Pre-trained Transformer), are trained on extensive datasets containing examples of code in various programming languages. These models can not only automatically transform the syntax and semantics of the code but also optimize it, taking into account the features of the target platform and performance requirements.

However, like any technology, this approach has its pros and cons. Let's examine them in more detail.

Advantages of AI Code Translation

Among the advantages of using AI for code translation are the following:

• Simplification of the code conversion process: Using AI for code conversion is significantly simpler and faster than creating a full-fledged rule-based translator. Traditional translators require meticulous development of syntactic and semantic rules for each programming language, which is time-consuming and resource-intensive. AI models, on the other hand, are initially trained on large volumes of source code and can automatically adapt to various languages.

• Wide range of language pairs: AI tools can work with virtually any pairs of programming languages. This makes them versatile and flexible for use in various projects.

For example, with the help of an AI translator, you can easily convert C# code:

public class Calculator
{
    public int Add(int a, int b)
    {
        return a + b;
    }
}

To Rust:

struct Calculator;

impl Calculator {
    fn add(&self, a: i32, b: i32) -> i32 {
        a + b
    }
}

Or to Haskell:

module Calculator where

add :: Int -> Int -> Int
add a b = a + b

• Handling complex constructs: By understanding the context in which the translated code is used, AI can recognize and correctly handle complex syntactic and semantic constructs, which can be challenging for rule-based translators.

Consider translating C# code that contains the yield return statement to C++:

public IEnumerable<int> GetNumbers()
{
    for (int i = 0; i < 5; i++)
    {
        yield return i;
    }
}

public void PrintNumbers()
{
    foreach (int number in GetNumbers())
    {
        Console.WriteLine(number);
    }
}

In C++, there is no direct equivalent of yield, so the AI translator creates a vector and fills it with values to return:

std::vector<int> getNumbers() 
{
    std::vector<int> numbers;
    for (int i = 0; i < 5; ++i) 
    {
        numbers.push_back(i);
    }
    return numbers;
}

void printNumbers() 
{
    for (int number : getNumbers()) 
    {
        std::cout << number << std::endl;
    }
}

This is not exactly the same as a generator in C#, but it achieves a similar result. Implementing such logic in a rule-based translator would be very difficult. Moreover, in many other cases, using a vector of returned values is not suitable, such as when working with large data volumes:

public IEnumerable<int> GetAllNumbers()
{
    for (int i = 0; i < int.MaxValue; i++)
    {
        yield return i;
    }
}

public void PrintNumbers()
{
    foreach (int number in GetAllNumbers().Take(5))
    {
        Console.WriteLine(number);
    }
}

In this case, the AI translator proposes a completely different implementation in C++:

class NumberGenerator 
{
    public:
    class Iterator 
    {
        public:
        Iterator(int value) : value(value) {}
        int operator*() const { return value; }
        Iterator& operator++() { ++value; return *this; }
        bool operator!=(const Iterator& other) const { return value != other.value; }
        private:
        int value;
    };

    Iterator begin() const { return Iterator(0); }
    Iterator end() const { return Iterator(INT_MAX); }
};

void PrintNumbers() 
{
    NumberGenerator generator;
    int count = 0;
    for (int number : generator) 
    {
        if (count++ >= 5)
            break;
        
        std::cout << number << std::endl;
    }
}

As you can see, understanding the context is crucial when choosing the right way to implement code translation from one programming language to another. In this case, the AI translator was able to propose an approach that preserves the functionality of the original code by using lazy generation of numbers in C++, which helps avoid memory and performance issues.

Consider the following example demonstrating method overloading in C#:

public void ProcessData(int number) 
{
    Console.WriteLine("Processing integer: " + number);
}

public void ProcessData(string text) 
{
    Console.WriteLine("Processing string: " + text);
}

public void ProcessData(double number)
{
    Console.WriteLine("Processing double: " + number);
}

ProcessData(5);
ProcessData("Hello");
ProcessData(3.14);

// Output:
// Processing integer: 5
// Processing string: Hello
// Processing double: 3.14

Translating this code directly to Python is not possible due to the lack of method overloading support. However, the AI translator handles this by using dynamic typing and type checking to achieve similar functionality:

def process_data(data):
    if isinstance(data, int):
        print("Processing integer:", data)
    elif isinstance(data, str):
        print("Processing string:", data)
    elif isinstance(data, float):
        print("Processing double:", data)
    else:
        print("Unknown type")

process_data(5)
process_data("Hello")
process_data(3.14)

# Output:
# Processing integer: 5
# Processing string: Hello
# Processing double: 3.14

• Code optimization: The AI can suggest more optimal solutions for specific tasks, considering the features of the target programming language.

Consider the following Java code:

List<Integer> numbers = new ArrayList<>();
numbers.add(1);
numbers.add(2);
numbers.add(3);
numbers.add(4);
numbers.add(5);

List<Integer> evenNumbers = new ArrayList<>();

for (Integer number : numbers) 
{
    if (number % 2 == 0) 
    {
        evenNumbers.add(number);
    }
}

System.out.println(evenNumbers);

When translating it to Python, the AI can use list comprehensions for optimization:

numbers = [1, 2, 3, 4, 5]
even_numbers = [number for number in numbers if number % 2 == 0]

print(even_numbers)

Challenges and Limitations of Using AI for Code Translation

Despite all the advantages and capabilities, AI code translation has its drawbacks. Let's consider them:

• Dependence on training data: The quality of AI translation heavily depends on the data it was trained on. If the training data contains errors or does not cover all possible scenarios, this can negatively affect the result.

• Variability of results and testability: AI can produce different results for the same input values, making it difficult to test its performance, track changes in translation results, and predict its behavior.

Consider the following Python code:

def is_palindrome(s):
    return s == s[::-1]

word = "radar"
print(f"'{word}' is a palindrome: {is_palindrome(word)}")  # 'radar' is a palindrome: True

This can be translated by AI to C# either as:

public bool IsPalindrome(string s)
{
    char[] arr = s.ToCharArray();
    Array.Reverse(arr);
    return s == new string(arr);
}

string word = "radar";
Console.WriteLine($"'{word}' is a palindrome: {IsPalindrome(word)}");  // 'radar' is a palindrome: True

Or with the addition of an intermediate ReverseString() method, which was not mentioned in the original Python code:

public bool IsPalindrome(string s)
{
    return s == ReverseString(s);
}

public string ReverseString(string s)
{
    char[] arr = s.ToCharArray();
    Array.Reverse(arr);
    return new string(arr);
}

string word = "radar";
Console.WriteLine($"'{word}' is a palindrome: {IsPalindrome(word)}");  // 'radar' is a palindrome: True

In this case, the differences in the resulting code do not affect its functionality but can add confusion.

The fact is that with AI translation, the resulting code is not consistent. It can vary from run to run depending on various factors such as initial conditions or random parameters. This complicates the use of AI in stable and predictable systems. For example, if we make a small change to the original code, we expect to see the same small change in the resulting code when converted by a rule-based translator. However, when translating code using AI, the resulting code can differ significantly, including all identifier names and method implementations of the translated product.

To address this issue, special hints can be used in the code being converted to keep its critical parts, such as the public API, stable. Regular functional testing of the generated code can help ensure its correctness and functionality.

• Limitations on the volume of processed data: One of the most critical issues currently is the limited size of the AI model's context window. Here are the main reasons for this:

1. Limited data volume: The AI model's context window is restricted to a certain number of tokens. If the source file or project is too large, it may not fit into a single context window, making it difficult to process and translate large volumes of code.
2. Code fragmentation: Splitting a large source file or project into parts to fit into the context window can disrupt the integrity and coherence of the code, leading to errors and unpredictable behavior during translation.
3. Integration challenges: After translating individual parts of the code, there may be a need to integrate and check them for compatibility, adding an extra layer of complexity and requiring additional resources.
4. Complex dependency limitations: Large projects often have complex dependencies between various modules and components. The limited context window can make it difficult to properly understand and handle these dependencies, potentially leading to errors in the resulting code.
5. Need for additional validation: Due to possible errors and unpredictable changes in the generated code, additional validation and testing may be required, increasing time and resource costs.

Promising solutions to this problem include:

1. Modularization: Dividing a large project into smaller, independent modules can help fit each module into the context window.
2. Context optimization: Reducing and simplifying the code, removing redundant comments and unnecessary parts can help fit more useful information into the context window.
3. Using more powerful models: Some AI models have larger context windows. Using such models can help handle larger volumes of data.

• Privacy issues: Using an AI code translator can lead to data leaks if the source code is transmitted over the internet without reliable encryption. There is also a risk of the code being stored and misused by services, which can jeopardize your intellectual property rights to the transmitted code. To minimize these risks, it is important to use trusted services and carefully read their terms of use and privacy policies.

Conclusions

AI code translation offers high flexibility and significantly lower time and resource costs compared to creating a full-fledged rule-based translator for a specific language pair. This makes it a convenient tool for quickly converting code between different programming languages. However, its main drawback is the unpredictability of the results, which can complicate the use of the code in real projects where stability and predictability are critical factors. To minimize risks, it is recommended to use AI translation in combination with traditional methods of code testing and validation.