LVMapper: A Large-Variance Clone Detector Using Sequencing Alignment Approach

Wu, Ming; Wang, Pengcheng; Yin, Kangqi; Cheng, Haoyu; Xu, Yun; Roy, Chanchal K.

doi:10.1109/access.2020.2971545

Cited by 19 publications

(14 citation statements)

References 41 publications

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“…LV-Mapper [9] examines all operational modules and accumulates all seeds instead of tokens to discover clone pairs. These seeds are transformed into hash values and saved in a hash table.…”

Section: Detecting Cpsmentioning

confidence: 99%

“…Appache opennlp-master 1.9.1 is a natural language information retrieval toolkit based on machine learning. Among the most common NLP operations supported are indexing, sentence classification, part-of-speech labelling, named entity identification, stacking, and processing [25] Using these datasets, the proposed method's efficiency is compared to that of existing methods such as LV-CCD [9], ES-CCD [13], TBCNN-CCD [14], and CPVDetector [15] terms of accuracy, precision, recall, time period, memory space, and clone types for detecting all four types of CC detection and its similarity features.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…A unique and effective detector called LV-Mapper [9] known as LV-CCD uses a locate-filterverify technique which locates and filters probable clone code by using a restricted window of continuous lines known as seeds which determines the dynamic threshold for CC verification. It detects Type-1 to Type-3 clones more accurately.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

2023

IJIES

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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.

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“…LV-Mapper [9] examines all operational modules and accumulates all seeds instead of tokens to discover clone pairs. These seeds are transformed into hash values and saved in a hash table.…”

Section: Detecting Cpsmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

See 1 more Smart Citation

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

2023

IJIES

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“…It specifically considers the modifications that are more scattered in large codes. LVMapper makes use of seeds (small windows of continuous lines) to located and filter the candidate pairs of code clones [9]. SourcererCC is a technique based on token level granularity that uses an index to achieve scalability.…”

Section: State Of the Art For Type 3 Clonesmentioning

confidence: 99%

SSA-HIAST: A Novel Framework for Code Clone Detection

Saini¹,

Singh²

2022

Computers, Materials &Amp; Continua

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In the recent era of software development, reusing software is one of the major activities that is widely used to save time. To reuse software, the copy and paste method is used and this whole process is known as code cloning. This activity leads to problems like difficulty in debugging, increase in time to debug and manage software code. In the literature, various algorithms have been developed to find out the clones but it takes too much time as well as more space to figure out the clones. Unfortunately, most of them are not scalable. This problem has been targeted upon in this paper. In the proposed framework, authors have proposed a new method of identifying clones that takes lesser time to find out clones as compared with many popular code clone detection algorithms. The proposed framework has also addressed one of the key issues in code clone detection i.e., detection of near-miss (Type-3) and semantic clones (Type-4) with significant accuracy of 95.52% and 92.80% respectively. The present study is divided into two phases, the first method converts any code into an intermediate representation form i.e., Hashinspired abstract syntax trees. In the second phase, these abstract syntax trees are passed to a novel approach "Similarity-based self-adjusting hash inspired abstract syntax tree" algorithm that helps in knowing the similarity level of codes. The proposed method has shown a lot of improvement over the existing code clones identification methods.

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“…PDG-based Techniques These techniques prepare the program dependency graph to represent the control and data flow of source code [47]. The technique has addressed the detection of type 4 clones.…”

Section: Token-based Techniquesmentioning

confidence: 99%

A Methodology for Reliable Code Plagiarism Detection Using Complete and Language Agnostic Code Clone Classification

Ankali¹,

Parthiban²

2021

IJMECS

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Code clone detection plays a vital role in both industry and academia. Last three decades have seen more than 250 clone detection techniques with lack of single framework that can detect and classify all 4 basic types of code clones with high precision. This serious lack of clone classification impacts largely on the universities and online learning platforms that fail to validate the projects or coding assignments submitted online. In this paper, we propose a complete and language agnostic technique to detect and classify all 4 clone types of C, C++, and Java programs. The method first generates the parse tree then extracts the functional tree to eliminate the need for the preprocessing stage employed by previous clone detection techniques. The generated parse tree contains all the necessary information for detecting code clones. We employ TF-IDF cosine similarity for the proper classification of clone types. The proposed technique achieves incredible precision rate of 100% in detecting the first two types of clones and 98% precision in detecting type-3 and type-4 clones for small codes of C, C++, and Java containing an average line count of 5. The proposed technique outperforms the existing tree-based clone detection tools by providing the average precision of 98.07% on the C, C++, and Java programs crawled from Github with an average line count of 15 which signifies that cosine similarity measure on ANTLR functional tree accurately detects all 4 types of small clones and act as proper validation tools for identifying the learning level in the submitted programming assignment.

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LVMapper: A Large-Variance Clone Detector Using Sequencing Alignment Approach

Cited by 19 publications

References 41 publications

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

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

SSA-HIAST: A Novel Framework for Code Clone Detection

A Methodology for Reliable Code Plagiarism Detection Using Complete and Language Agnostic Code Clone Classification

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