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Evaluation of Software Fault Prediction Models Considering Faultless Cases

机译：考虑故障案例的软件故障预测模型评估

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摘要

To enhance the prediction accuracy of the number of faults, many studies proposed various prediction models. The model is built using a dataset collected in past projects, and the number of faults is predicted using the model and the data of the current project. Datasets sometimes have many data points where the dependent variable, i.e., the number of faults is zero. When a multiple linear regression model is made using the dataset, the model may not be built properly. To avoid the problem, the Tobit model is considered to be effective when predicting software faults. The model assumes that the range of a dependent variable is limited and the model is built based on the assumption. Similar to the Tobit model, the Poisson regression model assumes there are many data points whose value is zero on the dependent variable. Also, log-transformation is sometimes applied to enhance the accuracy of the model. Additionally, ensemble methods are effective to enhance prediction accuracy of the models. We evaluated the prediction accuracy of the methods separately, when the number of faults is zero and not zero. In the experiment, our proposed ensemble method showed the highest accuracy, and Pred25 was 21% when the number of faults was not zero, and it was 45% when the number was zero.
机译：为了提高故障次数的预测准确性，许多研究提出了各种预测模型。该模型是使用过去项目中收集的数据集构建的模型，使用模型和当前项目的数据预测故障次数。数据集有时有许多数据点，其中依赖变量，即故障次数为零。当使用DataSet进行多元线性回归模型时，可能无法正确构建模型。为了避免问题，在预测软件故障时，Tobit模型被认为是有效的。该模型假设从属变量的范围是有限的，并且基于假设构建模型。类似于TOBBIT模型，泊松回归模型假定有许多数据点，其值在因变量上为零。此外，有时应用日志转换以增强模型的准确性。此外，集合方法可以有效地增强模型的预测精度。当故障次数为零而非零时，我们分别评估了方法的预测准确性。在实验中，我们所提出的集合方法显示出最高的精度，并且当故障的数量不为零时，Pred25为21％，当数字为零时，它为45％。

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