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首页> 外文期刊>International journal of open source software & processes >Empirical Evaluation of Bug Proneness Index Algorithm
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Empirical Evaluation of Bug Proneness Index Algorithm

机译：Bug恒指指数算法的实证评价

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摘要

Researchers have devised and implemented different bug prediction approaches that use different metrics to predict bugs in software modules. However, the focus of research has been on proposing new approaches/models to predict bugs rather than on validating performance of existing approaches. In this paper, the authors evaluate and validate the findings of an algorithm that predicts the bug proneness index (bug score) of the software classes/modules. The algorithm uses normalized marginal R square values of software metrics as weights to the normalized metrics to compute bug proneness index (bug score). The experiment was performed on Eclipse JDT Core and reports significant improvements in F-measure of their algorithm as compared to the multiple linear regression. The authors found that there was no improvement in F-measure of evaluated algorithm compared to multiple linear regression. The use of marginal R square values as weights to the metrics in linear functions in the evaluated model instead of regression coefficients had no performance boost compared to the multiple linear regression.
机译：研究人员设计并实施了使用不同指标的不同的错误预测方法来预测软件模块中的错误。然而，研究的重点是提出新的方法/模型来预测错误，而不是验证现有方法的性能。在本文中，作者评估并验证了一种预测软件类/模块的Bug恒指索引（错误分数）的算法的结果。该算法使用归一化的Marginal R平方值为归一化度量的权重，以计算Bug恒指索引（错误分数）。与多元线性回归相比，对Eclipse JDT核心进行了对Eclipse JDT核心的显着改进，并在其算法中报告了算法的重大改进。作者发现，与多个线性回归相比，评估算法的F测量没有改善。与多元线性回归相比，使用边缘R平方值为评估模型中的线性函数中的线性函数中的指标没有性能提升。

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