@Article{cmc.2020.09950,
AUTHOR = {Ye Zhai, Dongsheng Liu, Celimuge Wu, Rongrong She},
TITLE = {A Recommendation Approach Based on Bayesian Networks for  Clone Refactor},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {64},
YEAR = {2020},
NUMBER = {3},
PAGES = {1999--2012},
URL = {http://www.techscience.com/cmc/v64n3/39472},
ISSN = {1546-2226},
ABSTRACT = {Reusing code fragments by copying and pasting them with or without minor 
adaptation is a common activity in software development. As a result, software systems 
often contain sections of code that are very similar, called code clones. Code clones are 
beneficial in reducing software development costs and development risks. However, 
recent studies have indicated some negative impacts as a result. In order to effectively 
manage and utilize the clones, we design an approach for recommending refactoring
clones based on a Bayesian network. Firstly, clone codes are detected from the source 
code. Secondly, the clones that need to be refactored are identified, and the static and 
evolutions features are extracted to build the feature database. Finally, the Bayesian 
network classifier is used for training and evaluating the classification results. Based on 
more than 640 refactor examples of five open source software developed in C, we 
observe a considerable enhancement. The results show that the accuracy of the approach 
is larger than 90%. We believe our approach will provide a more accurate and reasonable 
code refactoring and maintenance advice for software developers.},
DOI = {10.32604/cmc.2020.09950}
}