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首页> 外文期刊>International journal of systems assurance engineering and management >Estimation of maintainability parameters for object-oriented software using hybrid neural network and class level metrics
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Estimation of maintainability parameters for object-oriented software using hybrid neural network and class level metrics

机译：使用混合神经网络和类级别指标评估面向对象软件的可维护性参数

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摘要

The various software metrics proposed in the literature can be used to evaluate the quality of software systems written in object-oriented manner. These metrics are broadly categorized into two subcategories i.e., system level software metrics and class level software metrics. In this work, ten different types of class level metrics are considered as an input to develop one model for predicting software maintainability of object-oriented software system. These models are developed using three types of neural networks, i.e., artificial neural network, radial basis function network, and functional link artificial neural network. In this study, a hybrid algorithm based on genetic algorithm (GA) with gradient descent algorithm has been proposed to find optimal weights of these neural networks. Since accuracy of the prediction model is highly dependent on the class level metrics, they are considered as input of the models. So, five different feature selection techniques are used in this study to identify the best set of features with an objective to improve the accuracy of software maintainability prediction model. The effectiveness of these models are evaluated using four evaluation metrics, i.e., MAE, MMRE, RMSE, and SEM. In this work, parallel computing concept has been also considered with an objective to reduce the model training time. The results show that the model developed using the proposed hybrid algorithm based on GA with gradient descent algorithm give better results as compared to the work presented by other authors in literature. The results also show that feature selection techniques obtain better results for predicting maintainability as compared to all metrics. The experimental results show that parallel computing is beneficial in reducing the model training time.
机译：文献中提出的各种软件指标可用于评估以面向对象的方式编写的软件系统的质量。这些度量大致分为两个子类别，即系统级软件度量和类级软件度量。在这项工作中，将十种不同类型的类级别度量标准视为开发一种模型的模型，该模型用于预测面向对象软件系统的软件可维护性。这些模型是使用三种类型的神经网络开发的，即人工神经网络，径向基函数网络和功能链接人工神经网络。在这项研究中，提出了一种基于遗传算法与梯度下降算法的混合算法，以找到这些神经网络的最佳权重。由于预测模型的准确性高度依赖于类级别的度量，因此它们被视为模型的输入。因此，本研究使用五种不同的特征选择技术来确定最佳特征集，目的是提高软件可维护性预测模型的准确性。这些模型的有效性使用四个评估指标进行评估，即MAE，MMRE，RMSE和SEM。在这项工作中，还考虑了并行计算概念，目的是减少模型训练时间。结果表明，与文献中其他作者的工作相比，使用基于遗传算法和梯度下降算法的混合算法开发的模型具有更好的结果。结果还表明，与所有指标相比，特征选择技术可更好地预测可维护性。实验结果表明，并行计算有利于减少模型训练时间。

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