A detection framework for semantic code clones and obfuscated code

Sheneamer, Abdullah; Roy, Swarup; Kalita, Jugal

doi:10.1016/j.eswa.2017.12.040

Cited by 32 publications

(13 citation statements)

References 19 publications

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“…As tree based, metric based and semantic approaches can detect all types of clones. However, in only 6 studies ( [49], [51], [60], [63], [67], [69]) out of 54 studies, all types of clones are detected. Therefore, this area is still open.…”

Section: Discussion and Limitationsmentioning

confidence: 99%

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A Systematic Review on Code Clone Detection

et al. 2019

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Code cloning refers to the duplication of source code. It is the most common way of reusing source code in software development. If a bug is identified in one segment of code, all the similar segments need to be checked for the same bug. Consequently, this cloning process may lead to bug propagation that significantly affects the maintenance cost. By considering this problem, code clone detection (CCD) appears as an active area of research. Consequently, there is a strong need to investigate the latest techniques, trends, and tools in the domain of CCD. Therefore, in this paper, we comprehensively inspect the latest tools and techniques utilized for the detection of code clones. Particularly, a systematic literature review (SLR) is performed to select and investigate 54 studies pertaining to CCD. Consequently, six categories are defined to incorporate the selected studies as per relevance, i.e., textual approaches (12), lexical approaches (8), treebased approaches (3), metric-based approaches (7), semantic approaches (7), and hybrid approaches (17). We identified and analyzed 26 CCD tools, i.e., 13 existing and 13 proposed/developed. Moreover, 62 opensource subject systems whose source code is utilized for the CCD are presented. It is concluded that there exist several studies to detect type1, type2, type3, and type4 clones individually. However, there is a need to develop novel approaches with complete tool support in order to detect all four types of clones collectively. Furthermore, it is also required to introduce more approaches to simplify the development of a program dependency graph (PDG) while dealing with the detection of the type4 clones.INDEX TERMS CCD, SLR, code clone detection, CCD tools, code clone types.

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Section: Discussion and Limitationsmentioning

confidence: 99%

“…Sheneamer et al [67] introduce a framework for obfuscated and semantic clones by using machine learning. It is a combination of semantic and tree-based techniques and Java code is used as a dataset that transforms into BDG, AST and PDG.…”

Section: F Hybrid Approachesmentioning

confidence: 99%

A Systematic Review on Code Clone Detection

et al. 2019

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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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“…Recently, machine learning technique -especially deep learning technique -has been applied to clone detection. White [25].…”

Section: Related Workmentioning

confidence: 99%

Improving Maintenance-Consistency Prediction During Code Clone Creation

Zhang¹,

Khoo²,

Su³

2020

IEEE Access

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Developers frequently introduce code clones into software through the copy-and-paste operations during the software development phase in order to shorten development time. Not all such clone creations are beneficial to software maintenance, as they may introduce extra effort at the software maintenance phase, where additional care is needed to ensure consistent change among these clones; i.e., changes made to a piece of code may need to be propagated to other clones. Failure in doing so may risk introducing bugs into software, which are usually called consistent defect. In response to the rampant maintenance cost caused by the introduction of new clones, some researchers have advocated the use of machine-learning approach to predict the likelihood of consistent change requirement when clones are freshly introduced. Leading in this approach is the work by Wang et al., which uses Bayesian Network to model maintenance-consistency of newly introduced clones. In this work, we leverage the success of the above-mentioned work by providing a revised set of attributes that has been shown to strengthen the predictive power of the Bayesian network model, as determined more quantitatively by the precision and recall levels. We firstly provide the definition of clone consistency-maintenance requirement, which can help transfer this problem to a classification problem. Then, based on collecting all clone creation operations through traversing clone genealogies, we redesign the attribute sets for representing clone creation with more information in code and context perspective. We evaluate the effectiveness of our approach on four open source projects with more quantitative analysis, and the experimental results show that our approach possesses a powerful ability in predicting clone consistency. To transfer this work into practice, we develop an Eclipse plug-in tool of this prediction to aid developers in software development and maintenance.INDEX TERMS Code clones, clone consistent change, clone consistency prediction, software maintenance, machine learning.

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A detection framework for semantic code clones and obfuscated code

Cited by 32 publications

References 19 publications

A Systematic Review on Code Clone Detection

A Systematic Review on Code Clone Detection

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

Improving Maintenance-Consistency Prediction During Code Clone Creation

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