On the Application of Bandit Algorithm for Selecting Clone Detection Methods

Abstract

Various clone detection methods have been proposed, with results varying depending on the combination of the methods and hyperparameters used (i.e., configurations). To help select a suitable clone detection configuration, we propose two Bandit Algorithm (BA) based methods that can help evaluate the configurations used dynamically while using detection methods. Our analysis showed that the two proposed methods, the naïve method and BANC (BA considering Negative Cases), identified the best configurations from four used code clone detection methods with high probability.