Code clone is common in software development, which usually leads to software defects or copyright infringement. Researchers have paid significant attention to code clone detection, and many methods have been proposed. However, the patterns for generating the code clones do not always remain the same. In order to fool the clone detection systems, the plagiarists, known as the clone creator, usually conduct a series of tricky modifications on the code fragments to make the clone difficult to detect. The existing clone detection approaches, which neglects the dynamics of the “contest” between the plagiarist and the detectors, is doomed to be not robust to adversarial revision of the code. In this paper, we propose a novel clone detection approach, namely ACD, to mimic the adversarial process between the plagiarist and the detector, which enables us to not only build strong a clone detector but also model the behavior of the plagiarists. Such a plagiarist model may in turn help to understand the vulnerability of the current software clone detection tools. Experiments show that the learned policy of plagiarist can help us build stronger clone detector, which outperforms the existing clone detection methods.
Find Me if You Can: Deep Software Clone Detection by Exploiting the Contest between the Plagiarist and the Detector
