Automatically classifying source code using tree-based approaches

Phan, Anh Viet; Chau, Phuong Ngoc; Nguyen, Minh Le; Bui, Lam Thu

doi:10.1016/j.datak.2017.07.003

Cited by 11 publications

(9 citation statements)

References 29 publications

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“…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

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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.

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Section: Related Workmentioning

confidence: 99%

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

et al. 2020

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“…To select relevant features from the network traffic features shown in Table I, the author uses the feature importance approach [16,17]. Feature importance is the influence that a feature has in predicting the classification results [18].…”

Section: Methodsmentioning

confidence: 99%

DDoS Attacks Detection Method Using Feature Importance and Support Vector Machine

Sanmorino

Gustriansyah

Alie

2022

JUITA

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In this study, the author wants to prove the combination of feature importance and support vector machine relevant to detecting distributed denial-of-service attacks. A distributed denial-of-service attack is a very dangerous type of attack because it causes enormous losses to the victim server. The study begins with determining network traffic features, followed by collecting datasets. The author uses 1000 randomly selected network traffic datasets for the purposes of feature selection and modeling. In the next stage, feature importance is used to select relevant features as modeling inputs based on support vector machine algorithms. The modeling results were evaluated using a confusion matrix table. Based on the evaluation using the confusion matrix, the score for the recall is 93 percent, precision is 95 percent, and accuracy is 92 percent. The author also compares the proposed method to several other methods. The comparison results show the performance of the proposed method is at a fairly good level in detecting distributed denial-of-service attacks. We realized this result was influenced by many factors, so further studies are needed in the future.

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“…It improved the accuracy from 4.08 to 15.49% compared to the feature-based approach and from 1.2 to 12.39% compared with the tree-based approaches. In addition, they introduced the Tree-Based Convolutional Neural Networks (TBCNN) framework (Phan et al, 2018a), it enhanced the time and accuracy of classifiers by applying several pruning tree models. TBCNN achieved a better accuracy by 92.63% on average.…”

Section: E Frameworkmentioning

confidence: 99%

A Systematic Literature Review of Software Defect Prediction Using Deep Learning

Fathy¹,

Abd-Elmegid²,

Bahaa³

et al. 2021

Journal of Computer Science

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The approaches associated with software defect prediction are used to reduce the time and cost of discovering software defects in source code and to improve the software quality in the organizations. There are two approaches to reveal the software defects in the source code. The first approach is concentrated on the traditional features such as lines of code, code complexity, etc. However, these features fail to extract the semantics of the source code. The second one is concentrated on revealing these semantics. This paper presents a Systematic Literature Review (SLR) of software defect prediction using deep learning models. This SLR is focused on identifying the studies that use the semantics of the source code for improving defect prediction. This SLR aims to analyze the used datasets, models and frameworks. Also, identifying the evaluation metrics to ensure their applicability in software defect prediction. IEEE Xplore, Scopus and Web of Science digital libraries were used to select the suitable primary studies. Forty (40) primary studies were selected that published by 15 December 2020 for analysis based on the quality criteria. The project levels that applied in the studies were: Within-project 52.5%, cross-project 17.5% and both within-project and cross-project 30%. The datasets used were: Promise dataset 68.18% and other datasets 31.82%. The most used deep learning model in the primary studies was: Convolutional Neural Network (CNN) by 35%. The most used evaluation metrics were: F-measure and Area Under the Curve (AUC). Software defect prediction using deep learning models is still a valuable topic and requires much research studies to enhance the performance of the defect prediction.

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Automatically classifying source code using tree-based approaches

Cited by 11 publications

References 29 publications

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

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

DDoS Attacks Detection Method Using Feature Importance and Support Vector Machine

A Systematic Literature Review of Software Defect Prediction Using Deep Learning

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