Bug Predicting Survey Using Advanced Machine Learning Algorithms

Abstract

In dynamic software development, ensuring high reliability and quality is an important goal. The biggest challenge in this field is the early detection and correction of errors, which if not addressed in time can lead to significant cost and time savings The aim of this study is to investigate the use of advanced machine learning (ML) algorithms to predict bugs in software systems to increase the efficiency and accuracy of the software development cycle. Analysis performs comprehensive analyzes using a variety of sophisticated ML algorithms including neural networks, random Forests, support vector machines (SVMs), and gradient enhancement machines. These algorithms are used to predict the probability of a bug in the code. The methodology includes a comprehensive approach to data acquisition, preprocessing, feature engineering, model training, and validation. They use a rich data-set, including code parameters, change logs, and developer activity, obtained from several open-source software repositories and defect-tracking systems