Source Codes Classification Using a Modified Instruction Count Pass

Darwish, Omar; Maabreh, Majdi; Karajeh, Ola; Alsinglawi, Belal

doi:10.1007/978-3-030-15035-8_88

Cited by 1 publication

(1 citation statement)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Various studies have been conducted to analyze source codes such as generating pseudocode from source code [7], identifying topics in source codes [8], source code vulnerability analysis [9], code suggestion [10], classifying source codes according to their functionalities [11], clustering of source codes [12], code review [13], source code author identification [14] and summarization of code fragments [15]. Although all these studies used source codes as training dataset for learning task, they did not focus on identifying the languages of source codes.…”

Section: Related Workmentioning

confidence: 99%

Comparison of Image-Based and Text-Based Source Code Classification Using Deep Learning

et al. 2020

View full text Add to dashboard Cite

Source code classification (SCC) is a task to assign codes into different categories according to a criterion such as according to their functionalities, programming languages or vulnerabilities. Many source code archives are organized according to the programming languages, and thereby, the desired code fragments can be easily accessed by searching within the archive. However, manually organizing source code archives by field experts is labor intensive and impractical because of the fastgrowing available source codes. Therefore, this study proposes new convolutional neural network (CNN) architectures to build source code classifiers that automatically identify programming languages from source codes. This is the first study in which the performances of deep learning algorithms on programming language identification are compared on both image and text files. In this study, the experiments are performed on three source code datasets to identify eight programming languages, including C, C++, C# , Go, Python, Ruby, Rust, and Java. The comparative results indicate that although textbased SCC and image-based SCC approaches achieve very high ( > 93.5% ) and similar accuracies, text-based classification has significantly better performance in terms of execution time.

show abstract