Machine-Learning Aided Analysis of Clone Evolution

ZHANG Fanlong; KHOO Siau-Cheng; SU Xiaohong

doi:10.1049/cje.2017.08.012

Volume 26 Issue 6

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ZHANG Fanlong, KHOO Siau-Cheng, SU Xiaohong, “Machine-Learning Aided Analysis of Clone Evolution,” Chinese Journal of Electronics, vol. 26, no. 6, pp. 1132-1138, 2017, doi: 10.1049/cje.2017.08.012

Citation:

ZHANG Fanlong, KHOO Siau-Cheng, SU Xiaohong, “Machine-Learning Aided Analysis of Clone Evolution,” Chinese Journal of Electronics, vol. 26, no. 6, pp. 1132-1138, 2017, doi: 10.1049/cje.2017.08.012

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Machine-Learning Aided Analysis of Clone Evolution

doi: 10.1049/cje.2017.08.012

1.
School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China;
2.
School of Computing, National University of Singapore, Singapore

Funds: This work is supported by the National Natural Science Foundation of China (No.61173021).

More Information

Corresponding author: SU Xiaohong (corresponding author) was born in 1966. She is a professor at Harbin Institute of Technology. Her research interests include software fault localization, clone detection and analysis, program analysis. (Email:sxh@hit.edu.cn)
Received Date: 2016-01-21
Rev Recd Date: 2016-07-05
Publish Date: 2017-11-10

Abstract

Abstract

Code clones are similar code fragments appearing in software. As software evolves, code clones may be subjected to changes as well; we term this clone evolution. There have not been many investigations into clone evolution characteristics. Therefore, we tackle this by exploring useful information associated with changes of clones during evolution. We focus on three perspectives of clone evolution, ranging from individual clone changes to characterization of clone genealogies. With the help Xmeans clustering, we establish associations between clone changes and life of clones. Our experimental results on two softwares show that clones are mostly stable throughout software evolution. For the relatively smaller group of "unstable" clones, changes usually happen after several versions, and consistent changes appear more frequently than inconsistent ones. We suggest that developers should pay more attention to relatively longer genealogies, and should consider applying changes consistently to clone group when a constituent clone fragment has undergone change.
- Code clones,
- Clone analysis,
- Clone characteristic,
- Clone metrics,
- Machine learning

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References(17)

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