VarCLR: Variable Semantic Representation Pre-training via Contrastive Learning

Last update: Oct 24, 2022

Overview

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 [email protected].

VarCLR: Variable Representation Pre-training via Contrastive Learning

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 [email protected]:

@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}
}

VarCLR: Variable Semantic Representation Pre-training via Contrastive Learning

Related tags

Overview

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

Results on IdBench benchmarks

Similarity

Relatedness

Pre-train your own VarCLR models

Cite

Owner

squaresLab

CVPR 2021 Challenge on Super-Resolution Space

Source code for NAACL 2021 paper "TR-BERT: Dynamic Token Reduction for Accelerating BERT Inference"

Locationinfo - A script helps the user to show network information such as ip address

Datasets and pretrained Models for StyleGAN3 ...

Codebase to experiment with a hybrid Transformer that combines conditional sequence generation with regression

Jittor implementation of PCT:Point Cloud Transformer

The repo of the preprinting paper "Labels Are Not Perfect: Inferring Spatial Uncertainty in Object Detection"

L-Verse: Bidirectional Generation Between Image and Text

Infrastructure as Code (IaC) for a self-hosted version of Gnosis Safe on AWS

Structured Data Gradient Pruning (SDGP)

ICCV2021 Oral SA-ConvONet: Sign-Agnostic Optimization of Convolutional Occupancy Networks

Get started with Machine Learning with Python - An introduction with Python programming examples

The ARCA23K baseline system

NALSM: Neuron-Astrocyte Liquid State Machine

IDRLnet, a Python toolbox for modeling and solving problems through Physics-Informed Neural Network (PINN) systematically.

This repo provides the base code for pytorch-lightning and weight and biases simultaneous integration.

Negative Sample Matters: A Renaissance of Metric Learning for Temporal Grounding

Aircraft design optimization made fast through modern automatic differentiation

The VarCNN is an Convolution Neural Network based approach to automate Video Assistant Referee in football.

Boosted CVaR Classification (NeurIPS 2021)