Machine-Learning Aided Analysis of Clone Evolution  

作　　者：ZHANG Fanlong KHOO Siau-Cheng SU Xiaohong 

机构地区：[1]School of Computer Science and Technology, Harbin Institute of Technology [2]School of Computing, National University of Singapore

出　　处：《Chinese Journal of Electronics》2017年第6期1132-1138,共7页电子学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(No.61173021)

摘　　要：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 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.

关 键 词：Code clones Clone analysis Clone characteristic Clone metrics Machine learning 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] TP311.5[自动化与计算机技术—控制科学与工程]