Timing-Based Clone Detection on Android Markets

2015 ◽  
pp. 375-381 ◽  
Author(s):  
Yingjun Zhang ◽  
Kezhen Huang ◽  
Yuling Liu ◽  
Kai Chen ◽  
Liang Huang ◽  
...  
Keyword(s):  
Clone Detection

CODE CLONE DETECTION USING OBJECT ORIENTED METRICS

2020 ◽  
Vol 9 (6) ◽  
pp. 3925-3931
Author(s):  
S. Sharma ◽  
D. Rattan ◽  
K. Singh
Keyword(s):  
Object Oriented ◽  
Clone Detection ◽  
Code Clone ◽  
Object Oriented Metrics

Open-source tools and benchmarks for code-clone detection

2020 ◽  
Vol 19 (4) ◽  
pp. 28-39 ◽  
Author(s):  
Andrew Walker ◽  
Tomas Cerny ◽  
Eungee Song
Keyword(s):  
Open Source ◽  
Clone Detection ◽  
Code Clone

scholarly journals Two-Pass Technique for Clone Detection and Type Classification Using Tree-Based Convolution Neural Network

2021 ◽  
Vol 11 (14) ◽  
pp. 6613
Author(s):  
Young-Bin Jo ◽  
Jihyun Lee ◽  
Cheol-Jung Yoo
Keyword(s):  
Neural Network ◽  
Average Rate ◽  
Convolution Neural Network ◽  
Clone Detection ◽  
Code Clones ◽  
Classification Technique ◽  
Development Costs ◽  
Code Quality ◽  
Type Information ◽  
Type Classification

Appropriate reliance on code clones significantly reduces development costs and hastens the development process. Reckless cloning, in contrast, reduces code quality and ultimately adds costs and time. To avoid this scenario, many researchers have proposed methods for clone detection and refactoring. The developed techniques, however, are only reliably capable of detecting clones that are either entirely identical or that only use modified identifiers, and do not provide clone-type information. This paper proposes a two-pass clone classification technique that uses a tree-based convolution neural network (TBCNN) to detect multiple clone types, including clones that are not wholly identical or to which only small changes have been made, and automatically classify them by type. Our method was validated with BigCloneBench, a well-known and wildly used dataset of cloned code. Our experimental results validate that our technique detected clones with an average rate of 96% recall and precision, and classified clones with an average rate of 78% recall and precision.

scholarly journals Multi-threshold token-based code clone detection

2021 ◽  
Author(s):  
Yaroslav Golubev ◽  
Viktor Poletansky ◽  
Nikita Povarov ◽  
Timofey Bryksin
Keyword(s):  
Clone Detection ◽  
Code Clone

scholarly journals Metric level based code clone detection using optimized code manager

2018 ◽  
Vol 7 (2.27) ◽  
pp. 144
Author(s):  
Gundeep Kaur ◽  
Sumit Sharma
Keyword(s):  
Semantic Analysis ◽  
Performance Metrics ◽  
Object Oriented Programming ◽  
Software Systems ◽  
Clone Detection ◽  
Major Drawback ◽  
Syntax Analysis ◽  
Code Clone ◽  
Svm Algorithm ◽  
Optimized Code

Object-oriented programming today, is the main prototype in typical software development. Code Cloning defines generally, all through the designing and development of software systems. Detection can be based on Textual analysis, Lexical analysis, Syntax analysis, Semantic analysis, Hybrid analysis and Metric analysis. The major drawback of the present research is that it focuses more on fragments of copied code and does not focus on the aspect that the fragments of duplicated code are may be part of a larger replicated program structure. In this process, techniques take a lot of time and it creates complexity. In our research, a source code is then scanned for detecting various methods by adopting a “OPTIMIZED SVM ALGORITHM” and the method definitions are extracted and collected by means of a CLONE CODE and saved for further reference. To evaluate the performance parameters we calculate the LOC, the number of repetitions, and maximum and minimum LOC. To enhance the performance metrics precision recall, accuracy and reduce the error rate and time complexity  

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