Effective Code Clone Detection by Combining Typed Tokens with Contrastive Learning

Core Concepts

CC2Vec, a novel code encoding approach, can effectively detect both syntactic and semantic code clones by combining typed tokens and contrastive learning.

Abstract

The paper introduces CC2Vec, a novel code encoding approach designed to efficiently detect syntactic code clones while enhancing the capability for semantic code clone detection.

Key highlights:

• CC2Vec divides source code tokens into 15 different categories (i.e., typed tokens) based on syntactic types and applies two self-attention mechanism layers to encode them. This helps retain program details between tokens.
• CC2Vec performs contrastive learning to reduce the differences introduced by different code implementations, making it robust to changes in code structure for semantic code clones.
• CC2Vec can effectively detect simple code clones by simply calculating the cosine similarity of two codes' vectors. It can also detect semantic code clones by combining with a few neural networks.
• Experiments on BigCloneBench and Google Code Jam datasets show that CC2Vec outperforms various pretrain-based and deep learning-based code clone detectors in both detection accuracy and efficiency.

Stats

CC2Vec achieves a recall of 64% on Type-4 (semantic) code clones with 98% precision, outperforming other pretrain-based methods.
CC2Vec significantly surpasses traditional code clone detectors like SourcererCC in detecting Type-3 and Type-4 clones, with a recall of 81% and 64% respectively.
CC2Vec is about 100 times faster than ASTNN in predicting code clone pairs.

Quotes

"CC2Vec not only attains comparable performance to widely used semantic code clone detection systems such as ASTNN, SCDetector, and FCCA by simply fine-tuning, but also significantly surpasses these methods in both detection efficiency."
"Compared to six deep-learning-based code clone detectors, CC2Vec can achieve the best F1 score when using only a simple three-layer neural network as the classifier."

Key Insights Distilled From

CC2Vec: Combining Typed Tokens with Contrastive Learning for Effective Code Clone Detection

by Shihan Dou,Y... at arxiv.org 05-02-2024

https://arxiv.org/pdf/2405.00428.pdf

Deeper Inquiries

How can the typed token categorization in CC2Vec be further improved to better capture the semantic relationships between tokens

In CC2Vec, the typed token categorization can be further improved to better capture the semantic relationships between tokens by incorporating more advanced natural language processing techniques. One approach could be to implement contextual embeddings for tokens, such as using transformer-based models like BERT or GPT, which have shown great success in capturing contextual information in text data. By utilizing contextual embeddings, the model can better understand the relationships between tokens within the context of the entire code snippet, leading to a more nuanced representation of the code semantics. Additionally, incorporating syntax-aware embeddings that consider both the syntactic structure and the semantic meaning of tokens can further enhance the categorization process. This hybrid approach can provide a more comprehensive understanding of the code and improve the detection of semantic relationships between tokens.

What other techniques beyond contrastive learning could be explored to make the code encoder more robust to structural changes in semantic code clones

Beyond contrastive learning, another technique that could be explored to make the code encoder more robust to structural changes in semantic code clones is adversarial training. Adversarial training involves training the model against adversarial examples that are specifically designed to deceive the model. By exposing the model to these adversarial examples during training, the model learns to be more resilient to variations and perturbations in the input data. In the context of code clone detection, adversarial training can help the encoder learn to identify and differentiate between subtle variations in code structure that may occur in semantic code clones. This can improve the model's ability to generalize to unseen variations and enhance its robustness in detecting semantic similarities between code snippets.

How can the insights from CC2Vec's design be applied to improve code understanding and analysis tasks beyond clone detection, such as code summarization or code generation

The insights from CC2Vec's design can be applied to improve code understanding and analysis tasks beyond clone detection, such as code summarization or code generation, by leveraging the learned representations of code snippets. For code summarization, the encoded vectors can be used to identify the most important and relevant parts of the code, enabling the generation of concise summaries that capture the essence of the code snippet. By applying attention mechanisms and self-attention layers similar to those in CC2Vec, the model can focus on key tokens and relationships to generate informative summaries. In the case of code generation, the encoded representations can serve as a foundation for generating new code snippets based on specific requirements or tasks. By fine-tuning the encoder and incorporating generation models like GPT, the system can produce syntactically and semantically correct code based on the learned representations, facilitating automated code generation tasks.