Software Fault Imputation in Noisy and Incomplete Measurement Data

Abstract

This study examines the impact of noise on the evaluation of software quality imputation techniques. The imputation procedures evaluated in this work include Bayesian multiple imputation, mean imputation, nearest neighbor imputation, regression imputation, and REPTree (decision tree) imputation. These techniques were used to impute missing software measurement data for a large military command, control, and communications system dataset (CCCS). A randomized three-way complete block design analysis of variance model using the average absolute error as the response variable was built to analyze the imputation results. Multiple pairwise comparisons using Fisher and Tukey-Kramer tests were conducted to demonstrate the performance differences amongst the significant experimental factors. The underlying quality of data was a significant factor affecting the accuracy of the imputation techniques. Bayesian multiple imputation and regression imputation were top performers, while mean imputation was ineffective.