Analysis of Research Trends Towards Types of Code Clone Detection Techniques

Kamna Solanki; Sandeep Dalal

doi:10.17485/IJST/v16i7.2219

Article

Analysis of Research Trends Towards Types of Code Clone Detection Techniques

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v16i7.2219

Year: 2023, Volume: 16, Issue: 7, Pages: 468-475

Original Article

Analysis of Research Trends Towards Types of Code Clone Detection Techniques

Kamna Solanki^1*, Sandeep Dalal¹

¹Associate Professor, Maharshi Dayanand University, Rohtak, India

*Corresponding Author
Email: [email protected]

Received Date:18 November 2022, Accepted Date:28 December 2022, Published Date:20 February 2023

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

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.

Keywords: Code Clones; Clone Detection Techniques; Metric Based Clone Detection; Types of Clone Detection Techniques; Software Clones

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Copyright

© 2023 Solanki & Dalal. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)