Test-Based Clone Detection: an Initial Try on Semantically Equivalent Methods
Author(s) -
Guangjie Li,
Hui Liu,
Yanjie Jiang,
Jiahao Jin
Publication year - 2018
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2018.2883699
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
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%.
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