A Fast Detecting Method for Clone Functions Using Global Alignment of Token Sequences

In large software projects, proper source code reuse can make development more efficient, but a lot of duplicate code and error code reuse can be a major cause of difficult system maintenance. Efficient clone code detection for large project can help manage the project. However, most of the clone detection methods are difficult to perform on adaptive analysis that adjusts specificity or sensitivity according to the type of clone to be detected. Therefore, when a user wants to find a particular type of clone in a large project, they must analyze it repeatedly using various tools to adjust the options. In this study, we propose a clone detection system based on the global sequence alignment. Lex based token analysis models and global alignment algorithm-based clone detection models were able to detect not only exact matches but also various types of clones by setting lower bound scores. Using features of the global alignment score calculation method to eliminate functions that cannot be clone candidates in advance, alignment analysis was possible even for large projects, and the execution time was predicted. For clone functions, we visualized the matching area, which is the result of alignment analysis, to represent clone information more efficiently.