Neural architectures for resolving references in program code

1. 1
   Abstract Reference Resolution

   Understand reference resolution in code as problems of 'direct indexing' (finding the definition of a variable) and 'indirect indexing' (understanding how a reference changes through permutations, like renaming or reordering). This abstraction simplifies the problem for neural networks.
2. 2
   Select a Sequence Model

   Choose a suitable neural sequence model (e.g., Transformer, LSTM, or a variant) known for its ability to learn complex patterns and permutations. These models excel at understanding contextual relationships within sequences, which is crucial for code.
3. 3
   Generate Synthetic Benchmarks

   Create synthetic datasets that mimic real-world code reference scenarios. These benchmarks should clearly define inputs (code snippets with unresolved references) and outputs (code with resolved or rewritten references) to train and evaluate your chosen neural architecture.
4. 4
   Train the Neural Architecture

   Train your selected sequence model on the generated synthetic benchmarks. Focus on optimizing the model's ability to accurately predict the correct reference resolutions or transformations based on the input code context.
5. 5
   Integrate into Code Analysis Tools

   Deploy the trained model as a component within existing or new code analysis, decompilation, or reverse engineering tools. Use its predictions to automate reference rewriting, improve code understanding, or enhance security analysis workflows.

import tokenize
import io

def get_python_tokens(code_string):
    """
    Tokenizes a Python code string into a list of (token_type, token_value) tuples.
    This is a foundational step for any ML-based code analysis.
    """
    tokens = []
    try:
        for token_info in tokenize.generate_tokens(io.StringIO(code_string).readline):
            # Exclude encoding and end-of-file tokens for cleaner analysis
            if token_info.type not in [tokenize.ENCODING, tokenize.ENDMARKER]:
                tokens.append((tokenize.tok_name[token_info.type], token_info.string))
    except tokenize.TokenError as e:
        return f"Tokenization error: {e}"
    return tokens

# Example Python code snippet
code_example = """
def calculate_sum(a, b):
    result = a + b
    return result
"""

# Tokenize the example code
print(get_python_tokens(code_example))

• Paperarxiv.org