CodeBLEU

This repository contains an unofficial CodeBLEU implementation that supports Linux, MacOS and Windows. It is available through PyPI and the evaluate library.

Available for: Python, C, C#, C++, Java, JavaScript, PHP, Go, Ruby.

The code is based on the original CodeXGLUE/CodeBLEU and updated version by XLCoST/CodeBLEU. It has been refactored, tested, built for macOS and Windows, and multiple improvements have been made to enhance usability.

Metric Description

An ideal evaluation metric should consider the grammatical correctness and the logic correctness. We propose weighted n-gram match and syntactic AST match to measure grammatical correctness, and introduce semantic data-flow match to calculate logic correctness.
[from CodeXGLUE repo]

In a nutshell, CodeBLEU is a weighted combination of n-gram match (BLEU), weighted n-gram match (BLEU-weighted), AST match and data-flow match scores.

The metric has shown higher correlation with human evaluation than BLEU and accuracy metrics.

Installation

This library requires so file compilation with tree-sitter, so it is platform dependent.
Currently available for Linux (manylinux), MacOS and Windows with Python 3.8+.

The metrics is available as pip package and can be installed as indicated above:

pip install codebleu

or directly from git repo (require internet connection to download tree-sitter):

pip install git+https://github.com/k4black/codebleu.git

Usage

from codebleu import calc_codebleu

prediction = "def add ( a , b ) :\n return a + b"
reference = "def sum ( first , second ) :\n return second + first"

result = calc_codebleu([reference], [prediction], lang="python", weights=(0.25, 0.25, 0.25, 0.25), tokenizer=None)
print(result)
# {
#   'codebleu': 0.5537, 
#   'ngram_match_score': 0.1041, 
#   'weighted_ngram_match_score': 0.1109, 
#   'syntax_match_score': 1.0, 
#   'dataflow_match_score': 1.0
# }

where calc_codebleu takes the following arguments:

• refarences (list[str] or list[list[str]]): reference code
• predictions (list[str]) predicted code
• lang (str): code language, see codebleu.AVAILABLE_LANGS for available languages (python, c_sharp c, cpp, javascript, java, php, go and ruby at the moment)
• weights (tuple[float,float,float,float]): weights of the ngram_match, weighted_ngram_match, syntax_match, and dataflow_match respectively, defaults to (0.25, 0.25, 0.25, 0.25)
• tokenizer (callable): to split code string to tokens, defaults to s.split()

and outputs the dict[str, float] with following fields:

• codebleu: the final CodeBLEU score
• ngram_match_score: ngram_match score (BLEU)
• weighted_ngram_match_score: weighted_ngram_match score (BLEU-weighted)
• syntax_match_score: syntax_match score (AST match)
• dataflow_match_score: dataflow_match score

Alternatively, you can use k4black/codebleu from HuggingFace Spaces (codebleu package required):

import evaluate
metric = evaluate.load("dvitel/codebleu")

prediction = "def add ( a , b ) :\n return a + b"
reference = "def sum ( first , second ) :\n return second + first"

result = metric.compute([reference], [prediction], lang="python", weights=(0.25, 0.25, 0.25, 0.25))

Feel free to check the HF Space with online example: k4black/codebleu

Contributing

Contributions are welcome!
If you have any questions, suggestions, or bug reports, please open an issue on GitHub.

Make your own fork and clone it:

git clone https://github.com/k4black/codebleu

For development, you need to install library (for so file to compile) with test extra:
(require internet connection to download tree-sitter)

python -m pip install -e .[test]
python -m pip install -e .\[test\]  # for macos

For testing just run pytest:

python -m pytest

To perform a style check, run:

python -m isort codebleu --check
python -m black codebleu --check
python -m ruff codebleu
python -m mypy codebleu

License

This project is licensed under the terms of the MIT license.

Citation

Official CodeBLEU paper can be cited as follows:

@misc{ren2020codebleu,
      title={CodeBLEU: a Method for Automatic Evaluation of Code Synthesis}, 
      author={Shuo Ren and Daya Guo and Shuai Lu and Long Zhou and Shujie Liu and Duyu Tang and Neel Sundaresan and Ming Zhou and Ambrosio Blanco and Shuai Ma},
      year={2020},
      eprint={2009.10297},
      archivePrefix={arXiv},
      primaryClass={cs.SE}
}