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作者:

Li, Guangjie (Li, Guangjie.) | Liu, Hui (Liu, Hui.) | Jiang, Yanjie (Jiang, Yanjie.) | Jin, Jiahao (Jin, Jiahao.)

收录:

EI Scopus SCIE

摘要:

Most code clone detection approaches identify clones via static source code analysis. Such approaches are effective and efficient in detecting lexically similar clones. However, they are less effective in detecting semantic clones that are similar in functionality but different in implementation. As an initial try to detect semantic clones, in this paper, we propose a test-based approach to detecting methods that are semantically equivalent to API methods. For a given method m, we generate its test cases automatically and search for semantically equivalent API methods by running the generated test cases. If two methods generate the same output on each of the test cases, they are taken as semantically equivalent methods. One of the weakness of test-based clone detection is that it is often time consuming. To reduce the time complexity, we take the following measures. First, we focus on methods instead of arbitrary fragments. Second, for a given method, we only compare it against such API methods whose signatures are highly similar to that of the given method. We evaluate the proposed approach on 10 well-known applications. Evaluation results suggest that it is efficient and accurate, and its precision is up to 98%.

关键词:

test-driven lexical similarity Clone detection semantic equivalence

作者机构:

  • [ 1 ] [Li, Guangjie]Beijing Inst Technol, Sch Comp Sci & Technol, Beijing 100081, Peoples R China
  • [ 2 ] [Liu, Hui]Beijing Inst Technol, Sch Comp Sci & Technol, Beijing 100081, Peoples R China
  • [ 3 ] [Jiang, Yanjie]Beijing Inst Technol, Sch Comp Sci & Technol, Beijing 100081, Peoples R China
  • [ 4 ] [Jin, Jiahao]Beijing Inst Technol, Sch Comp Sci & Technol, Beijing 100081, Peoples R China
  • [ 5 ] [Li, Guangjie]Beijing Univ Technol, Sch Engn, Gengdan Inst, Beijing 101301, Peoples R China

通讯作者信息:

  • [Liu, Hui]Beijing Inst Technol, Sch Comp Sci & Technol, Beijing 100081, Peoples R China

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来源 :

IEEE ACCESS

ISSN: 2169-3536

年份: 2018

卷: 6

页码: 77643-77655

3 . 9 0 0

JCR@2022

JCR分区:1

被引次数:

WoS核心集被引频次: 5

SCOPUS被引频次: 9

ESI高被引论文在榜: 0 展开所有

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中文被引频次:

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