2020

Conférence

Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion

Code smells, also known as anti-patterns, are indicators of bad design solutions. However, two different experts may have different opinions not only about the smelliness of a particular software class but also about the smell type. This causes an uncertainty problem that should be taken into account. Unfortunately, existing works reject uncertain data that correspond to software classes with doubtful labels. Uncertain data rejection could cause a significant loss of information that could considerably degrade the performance of the detection process. Motivated by this observation and the good performance of the possibilistic K-NN classifier in handling uncertain data, we propose in this paper a new evolutionary detection method, named ADIPOK (Anti-pattern Detection and Identification using Possibilistic Optimized K-NN), that is able to cope with the uncertainty factor using the possibility theory. The comparative experimental results reveal the merits of our proposal with respect to four relevant state-of-the-art approaches.

@inproceedings{boutaib2020handling,
title={Handling uncertainty in code smells detection using a possibilistic SBSE approach},
author={Boutaib, Sofien and Bechikh, Slim and Coello, Carlos A. Coello and Hung, Chih-Cheng and Said, Lamjed Ben},
booktitle={Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion},
pages={303--304},
year={2020},
publisher={Association for Computing Machinery}
}