CrystalBLEU

Install

pip install crystalbleu

Usage

from collections import Counter
from nltk.util import ngrams
# 1. Import CrystalBLEU
from crystalbleu import corpus_bleu

# 2. Extract trivially shared n-grams
k = 500
# <tokenized_corpus> is a list of strings
# Extract all n-grams of length 1-4
all_ngrams = []
for n in range(1, 5):
    all_ngrams.extend(list(ngrams(tokenized_corpus, n)))
# Calculate frequencies of all n-grams
frequencies = Counter(all_ngrams)
trivially_shared_ngrams = dict(frequencies.most_common(k))

# 3. Calculate CrystalBLEU
crystalBLEU_score = corpus_bleu(
    references, candidates, ignoring=trivially_shared_ngrams)

---

Paper

Our paper on CrystalBLEU has been presented at the 37th IEEE/ACM International Conference on Automated Software Engineering (ASE), where it was awarded with an ACM SIGSOFT Distinguished Paper Award.

Download the PDF

---

Reproducing Paper Results

The scripts directory contains scripts that generate results shown in the paper, based on the figure or table number, or the dataset used.
To reproduce the results from the paper run the following scripts in order:

# Install dependencies
pip install -r requirements.txt

# Download and prepare the datasets
bash ./scripts/prepare_data.sh english french java python c

# Print the data in Table 1 and plot Figure 3
bash ./scripts/table1-figure3.sh

# Plot Figure 4
bash ./scripts/figure4.sh

# Print the first two rows of Table 2
bash ./scripts/table2.sh

# Print results from BigCloneBench:
# - the third row of Table 2, 
# - the right columns of Table 3,
# - rows 3 & 4 of Table 4,
# - the third row of Table 5, and
# - Figure 5
bash ./scripts/big_clone_bench.sh

# Print results from ShareCode:
# - the left columns of Table 3,
# - rows 1 & 2 of Table 4, and
# - rows 1 & 2 of Table 5
bash ./scripts/sharecode.sh

# Results from Concode:
# - the last row of Table 4,
# - the last row of Table 5, and
# - plot Figure 6
bash ./scripts/figure6.sh

# Plot Figure 7
bash ./scripts/figure7.sh

# Plot Figure 8
bash./scripts.figure8.sh

When running scripts/prepare_data.sh, you can select any subset of english, french, java, python, and c. This allows downloading and preparing only the selected datasets. Note: this might cause some scripts to crash if they cannot find the required data files.
Scripts are mostly independent of eachother, except for the following:

• To run figure8.sh, you need to first run big_clone_bench.sh and sharecode.sh.

---

Contents

• CodeBLEU/ contains the implementation of CodeBLEU, obtained from CodeXGLUE.
• clone_detection/ contains the BigCloneBench dataset, obtained from CodeXGLUE.
• concode/ contains the Concode dataset, obtained from CodeXGLUE.
• sc_clone/ is derived from the ShareCod dataset in the style of BigCloneBench.
• scripts/ contains the scripts needed to reproduce the results from our paper.
• extra/ contains the scripts that were not used in the paper.
• lang{0, 1, 2}.json are data files from ShareCode for each of C, C++, and Java languages.