OPABP-Optimizing Parameters, to Improve Accuracy in Bug Prediction using Machine Learning

Abstract

Predicting a bug and attaining a successful application is critical in today's scenario during the development phase of a program. This can only be accomplished by foreseeing some of the shortcomings in the early stages of development, resulting in software that is dependable, efficient, and of high quality. A challenging aspect is to develop a sophisticated model capable to determine the error and producing effective software. A few ML methods are utilized to achieve this, and they produce accuracy with both trained and test datasets. The novelty of this approach is to demonstrate the applicability of machine learning algorithms namely Neural Network, SVM, Decision Tree and Cubist in using different performance metrics i.e. R, R square, Root Mean Square Error, Accuracy and obtaining the optimal outcome-based algorithm for a Bug report on diversion dataset from PROMISE repository. Findings reveal that SVM is giving significantly higher accuracy among all the algorithms in the ANT dataset and integrates the existing work on detecting a bug in software by providing information about various aforementioned methods in bug prediction The proposed work is highlighting the accuracy obtained by the current approaches that are significant for research scholars and solution providers.