Software Quality Imputation in the Presence of Noisy Data

Abstract

The detrimental effects of noise in a dependent variable on the accuracy of software quality imputation techniques were studied. The imputation techniques used in this work were Bayesian multiple imputation, mean imputation, instance-based learning, regression imputation, and the REPTree decision tree. These techniques were used to obtain software quality imputations for a large military command, control, and communications system dataset (CCCS). The underlying quality of data was a significant factor affecting the accuracy of the imputation techniques. Multiple imputation and regression imputation were top performers, while mean imputation was ineffective