scholarly journals Jupyter Notebooks on GitHub: Characteristics and Code Clones

2021 ◽  
Vol 5 (3) ◽  
Author(s):  
Malin Källén ◽  
Tobias Wrigstad
Keyword(s):  
Code Clones

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.

Managing code clones using dynamic change tracking and resolution

2009 ◽  
Author(s):  
Michiel de Wit ◽  
Andy Zaidman ◽  
Arie van Deursen
Keyword(s):  
Dynamic Change ◽  
Code Clones

scholarly journals Integrated Reasoning Engine for Code Clone Detection

2014 ◽  
Vol 3 (2) ◽  
pp. 143-152 ◽  
Author(s):  
Naresh Babu Bynagari
Keyword(s):  
Clone Detection ◽  
Code Clones ◽  
High Pitch ◽  
Detection Process ◽  
Code Clone ◽  
Similar Code ◽  
Reasoning Engine

This article seeks to foray into the nitty-gritty of integrated reasoning for code clone detection and how it is effectively carried out, given the amount of analytics usually associated with such activities. Detection of codes requires high-pitch familiarity with cloning systems and their workings. Hence, discovering similar code segments that are often regarded and seen as code imitations (clone) is not an easy responsibility. More especially, this very detection process might possess key purposes in the context of susceptibility findings, refactoring, and imitation detecting. Through the voyage of discovery this article intends to expose you to, you will realize that identical code segments, more often than not described as code clones, appear to be a serious duty, especially for large code bases <1; 2; 3; 4>. There are certain approaches and deep technicalities that this sort of detection is known for. Still, from the avalanche of resources that formed the bedrock of this article, one would discover the easiest formula to adopt in maneuvering such strenuous issues.

Using Dynamic Time Warping to Detect Clones in Software Systems

2021 ◽  
Vol 9 (1) ◽  
pp. 20-36
Author(s):  
Mostefai Abdelkader
Keyword(s):  
Time Series ◽  
Dynamic Time Warping ◽  
Software Systems ◽  
Clone Detection ◽  
Code Clones ◽  
Time Warping ◽  
Code Clone ◽  
Software Modules ◽  
Software Clone Detection ◽  
Dynamic Time

Software clone detection is a widely researched area over the last two decades. Code clones are fragments of code judged similar by some metric of similarity. This paper proposes an approach for code clone detection using dynamic time warping technique (i.e., DTW). DTW is a well-known algorithm for aligning and measuring similarity of time series and it has been found effective in many domains where similarity plays an important role such as speech and gesture recognition. The proposed approach finds clones in three steps. First software modules are extracted. Then, the extracted modules are turned to time series. Finally, the time series are compared using the DTW algorithm to find clones. The results of the experiment conducted on a well-known Benchmark show that the approach can detect clones effectively in software systems.

Comparing Bug Replication in Regular and Micro Code Clones

2019 ◽  
Author(s):  
Judith F. Islam ◽  
Manishankar Mondal ◽  
Chanchal K. Roy ◽  
Kevin A. Schneider
Keyword(s):  
Code Clones

Investigating Context Adaptation Bugs in Code Clones

2019 ◽  
Author(s):  
Manishankar Mondal ◽  
Banani Roy ◽  
Chanchal K. Roy ◽  
Kevin A. Schneider
Keyword(s):  
Code Clones ◽  
Context Adaptation

Investigating Vector-Based Detection of Code Clones Using BigCloneBench

2018 ◽  
Author(s):  
Kazuki Yokoi ◽  
Eunjong Choi ◽  
Norihiro Yoshida ◽  
Katsuro Inoue
Keyword(s):  
Code Clones

scholarly journals DECKARD: Scalable and Accurate Tree-Based Detection of Code Clones

2007 ◽  
Author(s):  
Lingxiao Jiang ◽  
Ghassan Misherghi ◽  
Zhendong Su ◽  
Stephane Glondu
Keyword(s):  
Code Clones
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