Smart Ensemble Approach For Software Defect Prediction

Abstract

Software defect prediction is essential
for improving software quality and reducing testing
costs. The main goal is to detect and forward just
faulty modules to the testing phase. This study
presents an advanced ensemble-based model for
software fault prediction that integrates many
classifiers. The suggested approach utilizes a twostage
prediction technique to identify damaged
modules. Initially, four supervised machine learning
techniques are utilized: Random Forest, Support
Vector Machine, Naïve Bayes, and Artificial Neural
Network. These algorithms undergo repeated
parameter optimization to get maximal accuracy. In
the subsequent phase, the predicted accuracy of the
different classifiers is amalgamated into a voting
ensemble to get the final predictions. This ensemble
method enhances the precision and dependability of
fault forecasts. Seven historical defect datasets from
the NASA MDP repository, namely CM1, JM1,
MC2, MW1, PC1, PC3, and PC4, were used to
develop and assess the suggested defect prediction
system. The findings indicate that the suggested
intelligent system for each dataset attained
exceptional accuracy, surpassing twenty advanced
defect prediction approaches, including base
classifiers and ensemble algorithms.