VarGAN: Adversarial Learning of Variable Semantic Representations

Abstract

Variable names are of critical importance in code representation learning. However, due to diverse naming conventions, variables often receive arbitrary names, leading to long-tail, out-of-vocabulary (OOV), and other well-known problems. While the Byte-Pair Encoding (BPE) tokenizer has addressed the surface-level recognition of low-frequency tokens, it has not noticed the inadequate training of low-frequency identifiers by code representation models, resulting in an imbalanced distribution of rare and common identifiers. Consequently, code representation models struggle to effectively capture the semantics of low-frequency variable names. In this paper, we propose VarGAN, a novel method for variable name representations. VarGAN strengthens the training of low-frequency variables through adversarial training. Specifically, we regard the code representation model as a generator responsible for producing vectors from source code. Additionally, we employ a discriminator that detects whether the code input to the generator contains low-frequency variables. This adversarial setup regularizes the distribution of rare variables, making them overlap with their corresponding high-frequency counterparts in the vector space. Experimental results demonstrate that VarGAN empowers CodeBERT to generate code vectors that exhibit more uniform distribution for both low- and high-frequency identifiers. There is an improvement of 8% in similarity and relatedness scores compared to VarCLR in the IdBench benchmark. VarGAN is also validated in downstream tasks, where it exhibits enhanced capabilities in capturing token- and code-level semantics.