Karthik, Sekar (2023) A Collaborative method for Code Clone Detection Using Lexical, Syntactic, Semantic and Structural Features. International Journal of Intelligent Engineering and Systems, 16 (1). pp. 67-78.

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Abstract

Software code clones (CC) in software programs are degrading the performance of software systems. many
code clones detection (CCD) methods proposed in the literature detect only individual cloned types efficiently. This
paper proposes a collaborative code clones detection (CCCD) method by utilizing lexical, syntactic, semantic and
structural features for effectively identifying all types of clones including type-4. Initially, a large variance mapper
(LV-mapper) is utilized to identify clone pairs (CPs). Then CPs are converted into lexical features by directly applying
Word2vec. The synonyms of CPs are obtained using the WordNet tool and converted as semantic features by
Word2vec.Additionally, code size metrics (CZM) and object oriented metrics (OOM) are additionally measured as
structural features of a program’s code blocks (CBs). The syntactic features are extracted through the abstract syntax
tree (AST) from the source code. Finally, the joint feature vector is generated by combining all the features together.
In order to detect CCs in any new software, the joint feature vector of known clone type source codes is generated first
(training set) and then the joint feature vector of unknown clone types source codes is generated next. The Euclidean
distances between training and testing of joint feature vectors determine the clone type of test features. Finally, the
experimental outcome demonstrates that the proposed CCCD technique has an accuracy of 87.8%, 92.3% and 95.5%
for the dataset Apache Maven 3.8.3, Appache ant 1.10.12 and Opennlp-master 1.9.1, respectively, compared to the
existing LV-CCD, ES-CCD, TBCNN-CCD and CPVDetector methods.

Item Type: Article
Uncontrolled Keywords: Code clone detection, LV-mapper, Clone pairs, Joint feature vector, Euclidean distances
Divisions: PSG College of Arts and Science > Department of Computer Science
Depositing User: Mr Team Mosys
Date Deposited: 11 Mar 2023 06:53
Last Modified: 11 Mar 2023 06:53
URI: http://ir.psgcas.ac.in/id/eprint/1758

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