TY - GEN
T1 - On the feasibility of detecting cross-platform code clones via identifier similarity
AU - Cheng, Xiao
AU - Jiang, Lingxiao
AU - Zhong, Hao
AU - Yu, Haibo
AU - Zhao, Jianjun
PY - 2016/9/3
Y1 - 2016/9/3
N2 - More and more mobile applications run on multiple mobile operating systems to attract more users of different platforms. Although versions on different platforms are implemented in different programming languages (e.g., Java and Objective-C), there must be many code snippets that implement the similar business logic on different platforms. Such code snippets are called cross-platform clones. It is challenging but essential to detect such clones for software maintenance. Due to the practice that developers usually use some common identifiers when implementing the same business logic on different platforms, in this paper, we investigate the identifier similarity of the same mobile application on different platforms and provide insights about the feasibility of cross-platform clone detection via identifier similarity. In our experiment, we have analyzed the source code of 18 open-source cross-platform applications which are implemented on Android, iOS and Windows Phone, and find that the smaller KL-Divergence the application has, the more accurate the clones detected by identifiers will be.
AB - More and more mobile applications run on multiple mobile operating systems to attract more users of different platforms. Although versions on different platforms are implemented in different programming languages (e.g., Java and Objective-C), there must be many code snippets that implement the similar business logic on different platforms. Such code snippets are called cross-platform clones. It is challenging but essential to detect such clones for software maintenance. Due to the practice that developers usually use some common identifiers when implementing the same business logic on different platforms, in this paper, we investigate the identifier similarity of the same mobile application on different platforms and provide insights about the feasibility of cross-platform clone detection via identifier similarity. In our experiment, we have analyzed the source code of 18 open-source cross-platform applications which are implemented on Android, iOS and Windows Phone, and find that the smaller KL-Divergence the application has, the more accurate the clones detected by identifiers will be.
UR - http://www.scopus.com/inward/record.url?scp=85049409922&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85049409922&partnerID=8YFLogxK
U2 - 10.1145/2975961.2975967
DO - 10.1145/2975961.2975967
M3 - Conference contribution
AN - SCOPUS:85049409922
SN - 9781450345118
T3 - SoftwareMining 2016 - Proceedings of the 5th International Workshop on Software Mining, co-located with ASE 2016
SP - 39
EP - 42
BT - SoftwareMining 2016 - Proceedings of the 5th International Workshop on Software Mining, co-located with ASE 2016
A2 - Lucia, Lucia
A2 - Li, Ming
A2 - Wang, Xiaoyin
PB - Association for Computing Machinery, Inc
ER -