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Ridge and Lasso Regression Models for Cross-Version Defect Prediction

机译：跨版本缺陷预测的Ridge和Lasso回归模型

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摘要

Sorting software modules in order of defect count can help testers to focus on software modules with more defects. One of the most popular methods for sorting modules is generalized linear regression. However, our previous study showed the poor performance of these regression models, which might be caused by severe multicollinearity. Ridge regression (RR) can improve the prediction performance for multicollinearity problems. Lasso regression (LAR) is a worthy competitor to RR. Therefore, we investigate both RR and LAR models for cross-version defect prediction. Cross-version defect prediction is an approximate to real applications. It constructs prediction models from a previous version of projects and predicts defects in the next version. Experimental results based on 11 projects from the PROMISE repository consisting of 41 different versions show that: 1) there exist severe multicollinearity problems in the experimental datasets; 2) both RR and LAR models perform better than linear regression and negative binomial regression for cross-version defect prediction; and 3) compared with two best methods in our previous study for sorting software modules according to the predicted number of defects, RR has comparable performance and less model construction time.
机译：按缺陷计数的顺序对软件模块进行排序可以帮助测试人员专注于缺陷更多的软件模块。排序模块最流行的方法之一是广义线性回归。但是，我们先前的研究表明这些回归模型的性能较差，这可能是由严重的多重共线性引起的。 Ridge回归（RR）可以改善多共线性问题的预测性能。拉索回归（LAR）是RR的有力竞争者。因此，我们研究了RR和LAR模型，用于交叉版本缺陷预测。跨版本缺陷预测近似于实际应用。它根据项目的先前版本构建预测模型，并预测下一版本中的缺陷。基于来自41个不同版本的PROMISE存储库中的11个项目的实验结果表明：1）实验数据集中存在严重的多重共线性问题； 2）RR和LAR模型在预测交叉版本缺陷方面均优于线性回归和负二项式回归； 3）与我们先前研究中根据预测的缺陷数量对软件模块进行分类的两种最佳方法相比，RR具有可比的性能，并且模型构建时间更少。

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