Software Fault Imputation Accuracy in Noisy and Incomplete Measurement Data

摘要

This study demonstrates 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, instance-based learning, regression, and REPTree decision trees. These techniques were used to impute missing software measurement data for a large military command, control, and communications system dataset (CCCS). A three-wsy analysis of variance randomized-complete block design 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 blocking variables.