A Collaborative method for Code Clone Detection Using Lexical, Syntactic, Semantic and Structural Features

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.