作者
Mohammad Y Mhawish, Manjari Gupta
发表日期
2020/12/1
期刊
Journal of Computer Science and Technology
卷号
35
期号
6
页码范围
1428-1445
简介
Code smell detection is essential to improve software quality, enhancing software maintainability, and decrease the risk of faults and failures in the software system. In this paper, we proposed a code smell prediction approach based on machine learning techniques and software metrics. The local interpretable model-agnostic explanations (LIME) algorithm was further used to explain the machine learning model’s predictions and interpretability. The datasets obtained from Fontana et al. were reformed and used to build binary-label and multi-label datasets. The results of 10-fold cross-validation show that the performance of tree-based algorithms (mainly Random Forest) is higher compared with kernel-based and network-based algorithms. The genetic algorithm based feature selection methods enhance the accuracy of these machine learning algorithms by selecting the most relevant features in each …
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MY Mhawish, M Gupta - Journal of Computer Science and Technology, 2020