Towards a clone detection benchmark suite and results archive

2004 ◽  
Author(s):  
A. Lakhotia ◽  
Junwei Li ◽  
A. Walenstein ◽  
Yun Yang
Keyword(s):  
Clone Detection ◽  
Benchmark Suite

Adding a Reproducible Airplane Model to the Austin RCS Benchmark Suite

2020 ◽  
Author(s):  
Jon T. Kelley ◽  
Andrew Maicke ◽  
David A. Chamulak ◽  
Clifton C. Courtney ◽  
Ali E. Yilmaz
Keyword(s):  
Benchmark Suite

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

iMLBench: A Machine Learning Benchmark Suite for CPU-GPU Integrated Architectures

2020 ◽  
pp. 1-1
Author(s):  
Chenyang Zhang ◽  
Feng Zhang ◽  
Xiaoguang Guo ◽  
Bingsheng He ◽  
Xiao Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Benchmark Suite

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.

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