Mining revision histories to detect cross-language clones without intermediates
To attract more users on different platforms, many projects release their versions in multiple programming languages (e.g., Java and C#). They typically have many code snippets that implement similar functionalities, i.e., cross-language clones. Programmers often need to track and modify cross-langu...
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Main Authors: | , , , , |
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Format: | text |
Language: | English |
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Institutional Knowledge at Singapore Management University
2016
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Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/3438 https://ink.library.smu.edu.sg/context/sis_research/article/4439/viewcontent/ase16clcminer.pdf |
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Institution: | Singapore Management University |
Language: | English |
Summary: | To attract more users on different platforms, many projects release their versions in multiple programming languages (e.g., Java and C#). They typically have many code snippets that implement similar functionalities, i.e., cross-language clones. Programmers often need to track and modify cross-language clones consistently to maintain similar functionalities across different language implementations. In literature, researchers have proposed approaches to detect cross-language clones, mostly for languages that share a common intermediate language (such as the .NET language family) so that techniques for detecting single-language clones can be applied. As a result, those approaches cannot detect cross-language clones for many projects that are not implemented in a .NET language. To overcome the limitation, in this paper, we propose a novel approach, CLCMiner, that detects cross-language clones automatically without the need of an intermediate language. Our approach mines such clones from revision histories, which reflect how programmers maintain cross-language clones in practice. We have implemented a prototype tool for our approach and conducted an evaluation on five open source projects that have versions in Java and C#. The results show that CLCMiner achieves high accuracy and point to promising future work. |
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