codeSync-tool 0.1.1

A tool to detect the inconsistent between code and text in your source code

CodeSync: Bootstrapping Code-Text Pretrained Language Model to Detect Inconsistency Between Code and Comment

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CodeSync Package

CodeSync is trained on top of encoder-decoder model to learn from code-text pairs. It is a tool that uses for automated detection to identify inconsistencies between code and docstrings, and generates comprehensive summary sentence to replace the old ones.

Usage Scenario

Installation

Install the dependencies:

pip -r install requirements.txt

Inference

Since CodeSync is a Python package, users can use it by inference function.

from CodeSync.utils import inference

Parameters:

• input_file_path (str): the file path that contains source code if users want to check all the functions in there.
• raw_code (str): a sequence of source code if input_file_path is not given.
• language (str, required): the programming language. We support 10 popular programming languages such as Java, JavaScript, Python, Ruby, Rust, Golang, C#, C++, C, and PHP.
• output_file_path (str): if input_file_path is given, the results from our tool will be written in this path; otherwise, they will be printed on the screen.

Example

from CodeSync.utils import inference

code = """
def inject_func_as_unbound_method(class_, func, method_name=None):
    # This is actually quite simple
    if method_name is None:
        method_name = get_funcname(func)
    setattr(class_, method_name, func)

def e(message, exit_code=None):
    # Print an error log message.
    print_log(message, YELLOW, BOLD)
    if exit_code is not None:
        sys.exit(exit_code)
"""

inference(raw_code=code, language='python')

>>> Your code snippet function: inject_func_as_unbound_method
    Results: 
            UNMATCH!
            Recommended docstring:  Inject a function as an unbound method.
    -------------
    Your code snippet function: e
    Results: 
            MATCH!

Reference

More details can be found in our paper. If you use this code or our package, please consider citing us:

@article{codesync,
  title={Bootstrapping Code-Text Pretrained Language Model to Detect Inconsistency Between Code and Comment},
  author={},
  journal={},
  pages={},
  year={2023}
}

Contact us

If you have any questions, comments or suggestions, please do not hesitate to contact us.

• Website: fpt-aicenter
• Email: [e-mail]

License

MIT License