Schemes for Labeling Semantic Code Clones using Machine Learning

Sheneamer, Abdullah; Hazazi, Hanan; Roy, Swarup; Kalita, Jugal

doi:10.1109/icmla.2017.00-25

Cited by 6 publications

(2 citation statements)

References 17 publications

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“…AST has shown excellent results from syntax-level cloning detection, but it cannot effectively detect discontinuous code clones. Literature [13] implements a novel clustering function, which effectively [4] Parse each code fragment to AST AST LSTM T1-T4 code fragments White et al [5] Use ANTLR to tokenize code AST FBT olive trees RtvNN T1-T4 Method/file level Marastoni et al [6] Leverage Compared with [14,17], the CCDLC system further improves the data preprocessing and feature vector conversion process.…”

Section: Semantic-based Clone Detection Methodsmentioning

confidence: 99%

A Survey of Automatic Software Vulnerability Detection, Program Repair, and Defect Prediction Techniques

Shen

Chen

2020

Security and Communication Networks

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Open source software has been widely used in various industries due to its openness and flexibility, but it also brings potential software security problems. Together with the large-scale increase in the number of software and the increase in complexity, the traditional manual methods to deal with these security issues are inefficient and cannot meet the current cyberspace security requirements. Therefore, it is an important research topic for researchers in the field of software security to develop more intelligent technologies to apply to potential security issues in software. The development of deep learning technology has brought new opportunities for the study of potential security issues in software, and researchers have successively proposed many automation methods. In this paper, these automation technologies are evaluated and analysed in detail from three aspects: software vulnerability detection, software program repair, and software defect prediction. At the same time, we point out some problems of these research methods, give corresponding solutions, and finally look forward to the application prospect of deep learning technology in automated software vulnerability detection, automated program repair, and automated defect prediction.

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Section: Semantic-based Clone Detection Methodsmentioning

confidence: 99%

A Survey of Automatic Software Vulnerability Detection, Program Repair, and Defect Prediction Techniques

Shen

Chen

2020

Security and Communication Networks

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“…Moreover, the original datasets contain small numbers of instances of specific types, making them difficult to use for machine learning. To overcome this situation we extract additional method blocks from the original source codes and label them using a semi-supervised labelling method [49]. The details of the datasets are given in Table 3.…”

Section: A Datasetsmentioning

confidence: 99%

An Effective Semantic Code Clone Detection Framework Using Pairwise Feature Fusion

2021

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Code clone detection is important for effective software maintenance. The task is more challenging when clones are semantically similar (Type-IV) in nature, having no structural resemblance to each other. Most existing methods use sequence similarity and/or graph isomorphism between either Abstract Syntax Trees (AST) or Program Dependency Graphs (PDG) to detect Type-I, II and III clones. However, they are mostly unsuccessful in detecting semantic or Type-IV clones. In this work, we propose a novel detection framework using machine learning for automated detection of all four type of clones. The features extracted from a pair code blocks are combined for possible detection of a clone with respect to reference block. We use ASTs and PDGs features of both the code blocks to prepare labelled training samples after fusing the two feature vectors using three different alternatives. We use six (06) state-of-the-art classification models including Deep Convolutional Neural Network to assess the prediction performance of our scheme. To access the effectiveness of our framework we use seven (07) datasets and compare its performance with five state-of-the-art clone detectors. We also compare a large number of algorithms for code clone detection. Comparing the performance of a large number of machine learning techniques, ANN and non-ANN, using such features, and establishing that fusing of AST and PDG features gives competitive results using deep learning as well as boosted tree algorithms, and find that boosted tree algorithms like XGBOOT are quite competitive in clone detection. Experimental results demonstrate that our approach outperforms existing clone detection methods in terms of prediction accuracy.

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Deep learning approaches for bad smell detection: a systematic literature review

2023

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Schemes for Labeling Semantic Code Clones using Machine Learning

Cited by 6 publications

References 17 publications

A Survey of Automatic Software Vulnerability Detection, Program Repair, and Defect Prediction Techniques

A Survey of Automatic Software Vulnerability Detection, Program Repair, and Defect Prediction Techniques

An Effective Semantic Code Clone Detection Framework Using Pairwise Feature Fusion

Deep learning approaches for bad smell detection: a systematic literature review

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