Applying Variant Variable Regularized Logistic Regression for Modeling Software Defect Predictor

Abstract

Empirical studies on software defect prediction models have come up with various predictors. In this study we examined variable regularized factors in conjunction with Logistic regression. Our work was built on eight public NASA datasets commonly used in this field. We used one of the datasets for our learning classification out of which we selected the regularization factor with the best predictor model; we then used the same regularization factor to classify the other seven datasets. Our proposed algorithm Variant Variable Regularized Logistic Regression (VVRLR) and modified VVRLR; were then used in the following metrics to measure the effectiveness of our predictor model: accuracy, precision, recall and F-Measure for each dataset. We measured above metrics using three Weka models, namely: BayesianLogisticRegression, NaiveBayes and Simple Logistic and then compared these results with VVRLR. VRLR and modified VVRLR outperformed the weka algorithms per our metric measurements. The VVRLR produced the best accuracy of 100.00%, and an average accuracy of 91.65 %; we had an individual highest precision of 100.00%, highest individual recall of 100.00% and F-measure of 100.00% as the overall best with an average value of 76.41% was recorded by VVRLR for some datasets used in our experiments. Our proposed modified VVRLR and variant VVRLR algorithms for F-measures outperformed the three weka algorithms.