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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.

• VarCLR: Variable Representation Pre-training via Contrastive Learning
  • Step 0: Install
  • Step 1: Load a Pre-trained VarCLR Model
  • Step 2: VarCLR Variable Embeddings
    • Get embedding of one variable
    • Get embeddings of list of variables (supports batching)
  • Step 2: Get VarCLR Similarity Scores
    • Get similarity scores of N variable pairs
    • Get pairwise (N * M) similarity scores from two lists of variables
  • Step 3: Reproduce IdBench Benchmark Results
    • Load the IdBench benchmark
    • Compute VarCLR scores and evaluate
    • Let's compare with the original CodeBERT
  • Pre-train your own VarCLR models
  • Results on IdBench benchmarks
    • Similarity
    • Relatedness
  • Cite

Step 0: Install

pip install -e .

Step 1: Load a Pre-trained VarCLR Model

from varclr.models.model 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}

Pre-train your own VarCLR models

You can pretrain and get the same VarCLR model variants with the following code.

python -m varclr.pretrain --model avg --name varclr-avg
python -m varclr.pretrain --model lstm --name varclr-lstm
python -m varclr.pretrain --model bert --name varclr-codebert --sp-model split --last-n-layer-output 4 --batch-size 64 --lr 1e-5 --epochs 1

The training progress and test results will be presented in the wandb dashboard. For reference, our training curves look like the following:

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

Cite

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

@inproceedings{ChenVarCLR2022,
  author = {Chen, Qibin and Lacomis, Jeremy and Schwartz, Edward J. and Neubig, Graham and Vasilescu, Bogdan and {Le~Goues}, Claire},
  title = {{VarCLR}: {Variable} Semantic Representation Pre-training via Contrastive Learning},
  booktitle = {International Conference on Software Engineering},
  year = {2022},
  series = {ICSE '22}
}