Residual fault density prediction using regression methods
Residual fault density prediction using regression methods
复制标题
使用回归方法预测残余故障密度
DOI：
10.1109/issre.1996.558706
复制
发表时间：
1996
期刊：
影响因子：
--
通讯作者：
G. Knafl
中科院分区：
文献类型：
--
作者：
J. A. Morgan;G. Knafl
回归方法被用来模拟几个产品和测试过程的措施方面的残留故障密度。考虑的过程措施包括发现的故障密度，测试集的大小和各种覆盖措施，如块，决策和所有用途的覆盖。考虑的产品度量包括代码行以及块、决策和所有使用计数。这些产品/工艺措施的相对重要性，预测剩余故障密度进行评估，为一个特定的数据集。在这种情况下，只有选定的测试过程措施，特别是发现的故障密度和决策覆盖率，是重要的预测因素，而所有考虑的产品措施都是重要的。这些结果是基于对大量模型的考虑，特别是具有双向交互作用的二次响应面模型家族。模型选择基于“一次留一个”交叉验证，使用预测残差平方和（PRESS）标准。
Regression methods are used to model residual fault density in terms of several product and testing process measures. Process measures considered include discovered fault density, test set size and various coverage measures such as block, decision and all-uses coverage. Product measures considered include lines of code as well as block, decision and all-uses counts. The relative importance of these product/process measures for predicting residual fault density is assessed for a specific data set. Only selected testing process measures, in particular discovered fault density and decision coverage, are important predictors in this case while all product measures considered are important. These results are based on consideration of a substantial family of models, specifically, the family of quadratic response surface models with two-way interaction. Model selection is based on "leave one out at a time" cross-validation using the predicted residual sum of squares (PRESS) criterion.