TITLE: A Novel Multiple Ensemble Learning Models Based on Different Datasets for Software Defect Prediction 

ABSTRACT: Software testing is one of the important ways to ensure the quality of
software. It is found that testing cost more than 50% of overall project cost.
Effective and efficient software testing utilizes the minimum resources of
software. Therefore, it is important to construct the procedure which is not
only able to perform the efficient testing but also minimizes the utilization
of project resources. The goal of software testing is to find maximum defects
in the software system. More the defects found in the software ensure more
efficiency is the software testing Different techniques have been proposed to
detect the defects in software and to utilize the resources and achieve good
results. As world is continuously moving toward data driven approach for making
important decision. Therefore, in this research paper we performed the machine
learning analysis on the publicly available datasets and tried to achieve the
maximum accuracy. The major focus of the paper is to apply different machine
learning techniques on the datasets and find out which technique produce
efficient result. Particularly, we proposed an ensemble learning models and
perform comparative analysis among KNN, Decision tree, SVM and Na\"ive Bayes on
different datasets and it is demonstrated that performance of Ensemble method
is more than other methods in term of accuracy, precision, recall and F1-score.
The classification accuracy of ensemble model trained on CM1 is 98.56%,
classification accuracy of ensemble model trained on KM2 is 98.18% similarly,
the classification accuracy of ensemble learning model trained on PC1 is
99.27%. This reveals that Ensemble is more efficient method for making the
defect prediction as compared other techniques.
Software testing is one of the important ways to ensure the quality of
software. It is found that testing cost more than 50% of overall project cost.
Effective and efficient software testing utilizes the minimum resources of
software. Therefore, it is important to construct the procedure which is not
only able to perform the efficient testing but also minimizes the utilization
of project resources. The goal of software testing is to find maximum defects
in the software system. More the defects found in the software ensure more
efficiency is the software testing Different techniques have been proposed to
detect the defects in software and to utilize the resources and achieve good
results. As world is continuously moving toward data driven approach for making
important decision. Therefore, in this research paper we performed the machine
learning analysis on the publicly available datasets and tried to achieve the
maximum accuracy. The major focus of the paper is to apply different machine
learning techniques on the datasets and find out which technique produce
efficient result. Particularly, we proposed an ensemble learning models and
perform comparative analysis among KNN, Decision tree, SVM and Na\"ive Bayes on
different datasets and it is demonstrated that performance of Ensemble method
is more than other methods in term of accuracy, precision, recall and F1-score.
The classification accuracy of ensemble model trained on CM1 is 98.56%,
classification accuracy of ensemble model trained on KM2 is 98.18% similarly,
the classification accuracy of ensemble learning model trained on PC1 is
99.27%. This reveals that Ensemble is more efficient method for making the
defect prediction as compared other techniques.
A Novel Multiple Ensemble Learning Models Based on Different Datasets for Software Defect Prediction
http://arxiv.org/abs/2008.13114v1