Optimal Machine learning Model for Software Defect Prediction

Abstract

Machine Learning is a division of Artificial Intelligence which builds a system that learns from the data. Machine learning has the capability of taking the raw data from the repository which can do the computation and can predict the software bug. It is always desirable to detect the software bug at the earliest so that time and cost can be reduced. Feature selection technique wrapper and filter method is used to find the most optimal software metrics. The main aim of the paper is to find the best model for the software bug prediction. In this paper machine learning techniques linear Regression, Random Forest, Neural Network, Support Vector Machine, Decision Tree, Decision Stump are used and comparative analysis has been done using performance parameters such as correlation, R-squared, mean square error, accuracy for software modules named as ant, ivy, tomcat, berek, camel, lucene, poi, synapse and velocity. Support vector machine outperform as compare to other machine learning model.

Artificial Intelligence for Software Engineering: An Initial Review on Software Bug Detection and Prediction

“…The back-propagation learning technique has been used to build neural networks. Performance has also been investigated through predictive validity, misclassification rate, cost verification and achieved quality (Lamba and Mishra, 2019).…”

“…This was a software bug prediction model using gradient Neural network networks (Kumar and Gupta, 2016) descent adoptive learning back-propagation techniques. Pervious updates can be fetched using version control systems (Lamba and Mishra, 2019). The automation of software testing and evaluation can be implemented using various AI approaches (Fehlmann and Kranich, 2017).…”

Ferrer diagram based partitioning technique to decision tree using genetic algorithm

“…It uses feature selection to select minimum number of features and computed classification accuracy, mean square error. Support vector machine shows high classification accuracy and low mean square error [15].…”