A code smell is a surface indicator of an inherent problem in the system, most often due to deviation from standard coding practices on the developers part during the development phase. Studies observe that code smells made the code more susceptible to call for modifications and corrections than code that did not contain code smells. Restructuring the code at the early stage of development saves the exponentially increasing amount of effort it would require to address the issues stemming from the presence of these code smells. Instead of using traditional features to detect code smells, we use user comments to manually construct features to predict code smells. We use three Extreme learning machine kernels over 629 packages to identify eight code smells by leveraging feature engineering aspects and using sampling techniques. Our findings indicate that the radial basis functional kernel performs best out of the three kernel methods with a mean accuracy of 98.52.
翻译:代码气味是系统内在问题的一个表面指标,最常见的原因是开发者在开发阶段偏离了标准的编码做法。研究发现,代码气味使得代码比不包含代码气味的代码更容易要求修改和校正。在开发初期对代码进行重组,从而节省了解决代码气味问题所需的急剧增加的工作量。我们不使用传统特征检测代码气味,而是使用用户意见手工构建功能来预测代码气味。我们使用3个超过629包的极端学习机器内核,通过利用特征工程方面和取样技术来识别8种代码气味。我们的调查结果显示,在3个内核方法中,光基功能内核以98.52的平均值最佳表现。