A technique to detect multi-grained code clones

Yusuke, Yuki; Higo, Yoshiki; Kusumoto, Shoichi

doi:10.1109/iwsc.2017.7880510

Cited by 6 publications

(6 citation statements)

References 10 publications

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“…Code Clone Detection. Tools and techniques of code clone detection can be classified as six categories [8]: textual approaches [10,23,27,34,37,51,56,58], lexical approaches [35,44,45,50,55,59], tree-based approaches [16,36,57], metric-based approaches [38,48,49], semantic approaches [18,19,22,24,43,54] and hybrid approaches [9,20,21,31,32,41,46,53]. In particular, we introduce several works closely related to our work.…”

Section: Related Workmentioning

confidence: 99%

From Innovations to Prospects

Jia

Fan

et al. 2020

Proceedings of the 17th International Conference on Mining Software Repositories

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The great influence of Bitcoin has promoted the rapid development of blockchain-based digital currencies, especially the altcoins, since 2013. However, most altcoins share similar source codes, resulting in concerns about code innovations. In this paper, an empirical study on existing altcoins is carried out to offer a thorough understanding of various aspects associated with altcoin innovations. Firstly, we construct the dataset of altcoins, including source code repository, GitHub fork relation, and market capitalization (cap). Then, we analyze the altcoin innovations from the perspective of source code similarities. The results demonstrate that more than 85% of altcoin repositories present high code similarities. Next, a temporal clustering algorithm is proposed to mine the inheritance relationship among various altcoins. The family pedigrees of altcoin are constructed, in which the altcoin presents similar evolution features as biology, such as power-law in family size, variety in family evolution, etc. Finally, we investigate the correlation between code innovations and market capitalization. Although we fail to predict the price of altcoins based on their code similarities, the results show that altcoins with higher innovations reflect better market prospects. CCS CONCEPTS• Software and its engineering → Software evolution.

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Section: Related Workmentioning

confidence: 99%

From Innovations to Prospects

Jia

Fan

et al. 2020

Proceedings of the 17th International Conference on Mining Software Repositories

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“…Yuki et al [40] present a technique to detect multi-grained code clones. In the presented technique, Java files utilized as a dataset and Smith-Waterman algorithm utilized to identify the identical hash sequence.…”

Section: B Lexical Approachesmentioning

confidence: 99%

“…It supports source code written in Java and executed on a workstation with 2.40 GHz Intel Xeon CPU (8 logic processors), and 32.0 GB of memory. Output databases and experimental targets located on SSD [40]. VUDDY is implemented as a technique for the discovery of vulnerable clones but compared with clone detection tools to determine accuracy.…”

Section: G Clone Detection Toolsmentioning

confidence: 99%

“…However, it has some scalability and evaluation work limitations. Yuki et al [40] present a technique to detect multi-grained code clones and develop a tool Decresendo based on the proposed technique. This tool supported by token-based code clone detection approaches and can identify type1, type2 and type3 clones.…”

Section: Rq4: What Are the Advantages And Limitations Of Ccd Techniques And Tools?mentioning

confidence: 99%

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A Systematic Review on Code Clone Detection

et al. 2019

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Code cloning refers to the duplication of source code. It is the most common way of reusing source code in software development. If a bug is identified in one segment of code, all the similar segments need to be checked for the same bug. Consequently, this cloning process may lead to bug propagation that significantly affects the maintenance cost. By considering this problem, code clone detection (CCD) appears as an active area of research. Consequently, there is a strong need to investigate the latest techniques, trends, and tools in the domain of CCD. Therefore, in this paper, we comprehensively inspect the latest tools and techniques utilized for the detection of code clones. Particularly, a systematic literature review (SLR) is performed to select and investigate 54 studies pertaining to CCD. Consequently, six categories are defined to incorporate the selected studies as per relevance, i.e., textual approaches (12), lexical approaches (8), treebased approaches (3), metric-based approaches (7), semantic approaches (7), and hybrid approaches (17). We identified and analyzed 26 CCD tools, i.e., 13 existing and 13 proposed/developed. Moreover, 62 opensource subject systems whose source code is utilized for the CCD are presented. It is concluded that there exist several studies to detect type1, type2, type3, and type4 clones individually. However, there is a need to develop novel approaches with complete tool support in order to detect all four types of clones collectively. Furthermore, it is also required to introduce more approaches to simplify the development of a program dependency graph (PDG) while dealing with the detection of the type4 clones.INDEX TERMS CCD, SLR, code clone detection, CCD tools, code clone types.

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“…It is a very adaptive process in software development activities. Recently, many clone detection approaches have been developed, which detect code clones in different software systems. However, during the detection of clones, it is difficult to say which code fragment is original and which code fragment is copied.…”

Section: Introductionmentioning

confidence: 99%

IBFET: Index‐based features extraction technique for scalable code clone detection at file level granularity

2019

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Many techniques have been developed over the years to detect code clones in different software systems to maintain security measures. These techniques often require the source code to compare the subject system against a very large data set of big code. This paper presents index-based features extraction technique (IBFET) to detect code clones at a very large-scale level to billions of LOC at file level granularity. We performed preprocessing, indexing, and clone detection for more than 324 billion of LOC using a Hadoop distributed environment, which is quite faster and more efficient as compared to existing distributed indexing and clone detection techniques; meanwhile, it detects all three types of clones efficiently. The MapReduce rule of divide and conquer is used for a count and retrieve the similar features between different systems. We evaluated the execution time, scalability, precision, and recall of IBFET by using a well-known clone detection data set IJaDataset and BigCloneBench; furthermore, we compared the results with other state-of-the-art tools. Our approach is faster, flexible, scalable, and provides accurate results with high authenticity and can be implemented at a large-scale level. KEYWORDSbig code, clone detection, code similarity detection, plagiarism detection, software reuse, software security and maintenance 22

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A technique to detect multi-grained code clones

Cited by 6 publications

References 10 publications

From Innovations to Prospects

From Innovations to Prospects

A Systematic Review on Code Clone Detection

IBFET: Index‐based features extraction technique for scalable code clone detection at file level granularity

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