A HYBRID FEATURE SELECTION MODEL FOR SOFTWARE FAULT PREDICTION
A HYBRID FEATURE SELECTION MODEL FOR SOFTWARE FAULT PREDICTION
复制标题
DOI：
10.5121/ijcsa.2012.2203
复制
发表时间：
2012-04
影响因子：
--
通讯作者：
C. Akalya;K. E. Kannammal;B Surendiran
中科院分区：
文献类型：
--
作者：
C. Akalya;K. E. Kannammal;B Surendiran
软件故障预测在软件质量保证中起着至关重要的作用。识别错误模块有助于更好地专注于这些模块，并有助于提高软件的质量。随着软件复杂性的增加，特征选择对于从数据集中去除冗余、不相关和错误的数据非常重要。一般来说，特征选择主要是基于过滤器和包装器。本文提出了一种混合特征选择方法，该方法比传统方法具有更好的预测能力。NASA的公共数据集KC 1在promise软件工程库中可用。为了评估软件故障预测模型的性能，使用准确度、平均绝对误差（MAE）、均方根误差（RMSE）值。
Software fault prediction plays a vital role in software quality assurance. Identifying the faulty modules helps to better concentrate on those modules and helps improve the quality of the software. With increasing complexity of software nowadays feature selection is important to remove the redundant, irrelevant and erroneous data from the dataset. In general, Feature selection is done mainly based on filter and wrapper. In this paper a hybrid feature selection method is proposed which gives a better prediction than the traditional methods. NASA’s public dataset KC1 available at promise software engineering repository is used. To evaluate the performance of the software fault prediction models Accuracy, Mean absolute error (MAE), Root mean squared error (RMSE) values are used.