Symmetric bug prediction in software requirement by machine learning algorithms

In the software development, high impact on diverse industries are expanding at a fast pace. Early defect fixation and adjustment according to needs are essential to increase the performance, accuracy, durability and reliability of the software. Machine Learning (ML) approaches have used to analyze the Software Requirement (SR) in software development offer a great way to accelerate symmetric development and defect correction, especially because software failures are unpredictable. This research utilizes classification and regression-based models like Random Forest (RF), Artificial Neural Network (ANN), and Adaptive Moment Estimation (AME) to forecast bug resolving times from applicable asymmetric data attributes. ML models are used to predict defect resolution and feature completion time. The data takes into consideration several stages including issue detection, testing, and validation. The outcome of ML models, K-Nearest Neighbors (KNN) algorithm has an accuracy of 66% but the proposed approach of RF, ANN, and ADE illustrates an astonishing 98% accuracy. The dataset with symmetric attributes has trends and strong correlations. The proposed model result is better than other available software failure time prediction in terms of precision and accuracy, providing a sound to defect solution and prediction in SR.