A parallel deep learning-based code clone detection model.

Code clone detection is a crucial task in software development and maintenance. While deep learning-based methods have been proposed to tackle this problem, most of them neglect the time and memory consumption issues which can be significant when working with limited computational resources. Given the inability of recurrent neural networks to train in a parallel manner, this paper presents a parallel code clone detection model based on temporal convolutional networks. The proposed method splits the corresponding abstract syntax tree into a set of code statement sequences, utilizes a temporal convolutional neural network to generate representations containing complexity features found in the source code, and finally measures the distance between these representations. The proposed method is evaluated on a real-world dataset for code clone detection, and the experimental results demonstrate that it performs comparably to state-of-the-art methods while requiring significantly less time and memory costs. • Temporal convolutional network achieves similar code clone detection results to sequence neural network with less time and memory costs. • The performance of code clone detection task will increase while the length of token embeddings is increasing. • The recursive structure in the statement encoder can be simplified as a feedforward neural network. [ABSTRACT FROM AUTHOR]