Analysis of Research Trends Towards Types of Code Clone Detection Techniques

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