Refactoring Code Clone Detection

Othman, Zhala Sarkawt; Kaya, Mehmet

doi:10.1109/isdfs.2019.8757479

Cited by 1 publication

(3 citation statements)

References 11 publications

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“…Researchers have conducted diverse studies and presented several code clone detection techniques for improving the process of detecting code cloning (4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20) . There are distinct domains for these techniques.…”

Section: Related Workmentioning

confidence: 99%

“…There are distinct domains for these techniques. Diverse techniques for clone detection are reviewed in this section (4)(5)(6)(7)(8)(9)(10)(11)(12) .…”

Section: Related Workmentioning

confidence: 99%

“…This method had robustness for modifying the code. Furthermore, a matching algorithm was put forward based on 3 graph-based elements which had the potential for detecting the code cloning, and the precision obtained from the developed method was counted at 98% and recall was 97% (9) . Othman and Kaya aimed to implement the technique of detecting the clone by recognizing the clone code and replacing it with a single call to the function.…”

Section: Related Workmentioning

confidence: 99%

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Analysis of Research Trends Towards Types of Code Clone Detection Techniques

Solanki¹,

Dalal²

2023

IJST

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Objective: The key research objectives of this study are: (1.) To compare and contrast the research trend towards the tree, token, text, metric, and graph-based code clone detection techniques; (2.) To study the distribution of metric-based code clone detection techniques on various online repositories; (3.) To make a statistical analysis of the hybrid techniques available for clone detection. The overall objective is to investigate the research trends of code clone detection approaches. Methods: Various repositories like google scholar, IEEE, and ELSEVIER Digital Libraries were systematically examined to attain the results in terms of research articles published in various places like conferences, journals, etc. followed by the inclusion and exclusion criteria. Findings: (1.) The findings related to objective 1 depicted that 50% of total clone detection techniques are tree and graph-based Code Clone Detection techniques followed by 20% of text-based and 30% of token-based code-clone detection techniques (2.) The findings related to the second objective depicted that an equal percentage of 46% of research work related to metric-based code clone detection techniques has been published in journals and conferences. (3.) The findings related to the third objective showed that 43% of hybrid code clone detection techniques are based on machine learning techniques, 24% are based on neural networks, and 18% of techniques are data mining based followed by 15% nature inspired based algorithms. Novelty: The study conducted is novel in identifying and exploring those potential code clone detection techniques that are underutilized and least explored. The result of research questions will assist researchers to draw inferences regarding usage, application, research trends, future needs, and research directions.

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Section: Related Workmentioning

confidence: 99%

“…There are distinct domains for these techniques. Diverse techniques for clone detection are reviewed in this section (4)(5)(6)(7)(8)(9)(10)(11)(12) .…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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