• Journal of Software Assessment and Valuation
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• v.14 no.2
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• pp.35-44
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• 2018

Tools for detecting cross-language clones usually compare abstract-syntax-tree representations of source code, which lacks scalability. In order to compare large source code to a practical level, we need a similarity checking technique that works on a token level basis. In this paper, we define common tokens that represent all tokens commonly used in programming languages of different paradigms. Each source code of different language is then transformed into the list of common tokens that are compared. Experimental results using exEyes show that our proposed method using common tokens is effective in detecting cross-language clones.

• Journal of Software Assessment and Valuation
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• v.15 no.1
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• pp.43-53
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• 2019

BigCloneBench has recently been used for performance evaluation of code clone detection tool using machine learning. However, since BigCloneBench is not a benchmark that is optimized for machine learning, incorrect learning data can be created. In this paper, we have shown through experiments using machine learning that the set of Type-4 clone methods provided by BigCloneBench can additionally be found. Experimental results using Tree-Based Convolutional Neural Network show that our proposed method is effective in improving BigCloneBench's dataset.