VarCLR: Variable Semantic Representation Pre-training via Contrastive Learning

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Deep Learning embeddings source-code pre-training contrastive-learning icse2022
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VarCLR: Variable Representation Pre-training via Contrastive Learning

New: Paper accepted by ICSE 2022. Preprint at arXiv!

This repository contains code and pre-trained models for VarCLR, a contrastive learning based approach for learning semantic representations of variable names that effectively captures variable similarity, with state-of-the-art results on IdBench@ICSE2021.

Step 0: Install

pip install -e .

Step 1: Load a Pre-trained VarCLR Model

from varclr.models import Encoder
model = Encoder.from_pretrained("varclr-codebert")

Step 2: VarCLR Variable Embeddings

Get embedding of one variable

emb = model.encode("squareslab")
print(emb.shape)
# torch.Size([1, 768])

Get embeddings of list of variables (supports batching)

emb = model.encode(["squareslab", "strudel"])
print(emb.shape)
# torch.Size([2, 768])

Step 2: Get VarCLR Similarity Scores

Get similarity scores of N variable pairs

print(model.score("squareslab", "strudel"))
# [0.42812108993530273]
print(model.score(["squareslab", "average", "max", "max"], ["strudel", "mean", "min", "maximum"]))
# [0.42812108993530273, 0.8849745988845825, 0.8035818338394165, 0.889922022819519]

Get pairwise (N * M) similarity scores from two lists of variables

variable_list = ["squareslab", "strudel", "neulab"]
print(model.cross_score("squareslab", variable_list))
# [[1.0000007152557373, 0.4281214475631714, 0.7207341194152832]]
print(model.cross_score(variable_list, variable_list))
# [[1.0000007152557373, 0.4281214475631714, 0.7207341194152832],
#  [0.4281214475631714, 1.0000004768371582, 0.549992561340332],
#  [0.7207341194152832, 0.549992561340332, 1.000000238418579]]

Step 3: Reproduce IdBench Benchmark Results

Load the IdBench benchmark

from varclr.benchmarks import Benchmark

# Similarity on IdBench-Medium
b1 = Benchmark.build("idbench", variant="medium", metric="similarity")
# Relatedness on IdBench-Large
b2 = Benchmark.build("idbench", variant="large", metric="relatedness")

Compute VarCLR scores and evaluate

id1_list, id2_list = b1.get_inputs()
predicted = model.score(id1_list, id2_list)
print(b1.evaluate(predicted))
# {'spearmanr': 0.5248567181503295, 'pearsonr': 0.5249843473193132}

print(b2.evaluate(model.score(*b2.get_inputs())))
# {'spearmanr': 0.8012168379981921, 'pearsonr': 0.8021791703187449}

Let's compare with the original CodeBERT

codebert = Encoder.from_pretrained("codebert")
print(b1.evaluate(codebert.score(*b1.get_inputs())))
# {'spearmanr': 0.2056582946575104, 'pearsonr': 0.1995058696927054}
print(b2.evaluate(codebert.score(*b2.get_inputs())))
# {'spearmanr': 0.3909218857993804, 'pearsonr': 0.3378219622284688}

Results on IdBench benchmarks

Similarity

Method Small Medium Large
FT-SG 0.30 0.29 0.28
LV 0.32 0.30 0.30
FT-cbow 0.35 0.38 0.38
VarCLR-Avg 0.47 0.45 0.44
VarCLR-LSTM 0.50 0.49 0.49
VarCLR-CodeBERT 0.53 0.53 0.51
Combined-IdBench 0.48 0.59 0.57
Combined-VarCLR 0.66 0.65 0.62

Relatedness

Method Small Medium Large
LV 0.48 0.47 0.48
FT-SG 0.70 0.71 0.68
FT-cbow 0.72 0.74 0.73
VarCLR-Avg 0.67 0.66 0.66
VarCLR-LSTM 0.71 0.70 0.69
VarCLR-CodeBERT 0.79 0.79 0.80
Combined-IdBench 0.71 0.78 0.79
Combined-VarCLR 0.79 0.81 0.85

Pre-train your own VarCLR models

Coming soon.

Cite

If you find VarCLR useful in your research, please cite our paper@ICSE2022:

@misc{chen2021varclr,
      title={VarCLR: Variable Semantic Representation Pre-training via Contrastive Learning},
      author={Qibin Chen and Jeremy Lacomis and Edward J. Schwartz and Graham Neubig and Bogdan Vasilescu and Claire Le Goues},
      year={2021},
      eprint={2112.02650},
      archivePrefix={arXiv},
      primaryClass={cs.SE}
}
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Comments
  • Call `encode()` with GPU

    Call `encode()` with GPU

    It would be great if there is an option to call model.encode("squareslab") with GPU instead of CPU.

    If we call model.to(torch.device("cuda")), it doesn't work because return_dict is on CPU: https://github.com/squaresLab/VarCLR/blob/2fe9595c2c056310e7de376b61d61ecb683f8160/varclr/models/encoders.py#L121

    opened by victorcwai 2
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squaresLab
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