Prediction for Software Quality Open-Source Dataset Based on CART Approach Using Machine Learning Techniques

Abstract

Open-source dataset available for different source of platform.Software
quality is an important approach for the user as well as softwaredevelopers
also.In this paper the study of various classification
and regression tree (CART) method for software quality predictions.
In this method have been used for a new algorithm design which is
based on partitioning method. Among so many prediction methods
over the recently published data mining predictionsfor software
quality models such that classification and regression tree (CART),
deep neural network (DNN), hierarchical attention network (HAN),
multiple linear regression (MLR), stepwise regression (SR), artificial
neural network (ANN) and case-based reasoning (CBR).CART
algorithm to design an enhanced level of classification accuracy for
large (complex) data set when compared with prior to classical CART
concepts. CART can perform a balanced tree structure that is misclassify
the fault-prone modules to classification-tree models based
on Open-source dataset. The Open-source dataset used in Vote to
classify the various machine learning algorithms such as J48, Random
Forest, Logistic Model Tree. In this paper we also compute the best
accuracy for vote dataset. This paper presents details on the cart
algorithm to predict the quality of system in different ML algorithms.