Huading Ling¹, Aiping Zhang¹, Changchun Yin¹, Dafang Li^2,*, Mengyu Chang³

Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1561-1577, 2022, DOI:10.32604/iasc.2022.027349

Abstract With the rise of deep learning in recent years, many code clone detection (CCD) methods use deep learning techniques and achieve promising results, so is cross-language CCD. However, deep learning techniques require a dataset to train the models. The dataset is typically small and has a gap between real-world clones due to the difficulty of collecting datasets for cross-language CCD. This creates a data bottleneck problem: data scale and quality issues will cause that model with a better design can still not reach its full potential. To mitigate this, we propose a tree autoencoder (TAE) architecture. It uses unsupervised learning… More >

Neha Saini^*, Sukhdip Singh

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2999-3017, 2022, DOI:10.32604/cmc.2022.022659

Abstract In the recent era of software development, reusing software is one of the major activities that is widely used to save time. To reuse software, the copy and paste method is used and this whole process is known as code cloning. This activity leads to problems like difficulty in debugging, increase in time to debug and manage software code. In the literature, various algorithms have been developed to find out the clones but it takes too much time as well as more space to figure out the clones. Unfortunately, most of them are not scalable. This problem has been targeted… More >