CodeBLEU

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

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

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

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

As this library require so file compilation it is platform dependent.
Currently available for Linux (manylinux) and MacOS 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:

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 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.

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}
}