Bug priority prediction using deep ensemble approach

Abstract

A software bug is a fault in the programming of software or an application. Bugs cause problems ranging from stability to operability and are typically the result of human error during the programming process. They could be the result of a mistake or error, as well as a fault or defect. Software bugs should be discovered during the testing stage of the software development life cycle, but some may go undetected until after deployment. When addressing a bug, it is critical to consider its priority, which is determined manually. However, it was a difficult task, and making the wrong decision could lead to major software failures. Therefore, the primary goal of this study is to propose an ensemble approach for predicting bug priority levels in bug reports. We make use of Bugzilla's dataset, which includes over 25,000 bug reports. After preprocessing the data, this study applies a variety of feature extraction techniques, including Glove, Word2Vec TF-IDF, and Doc2Vec. Then, a model that primarily employs seven architectures of Convolutional Neural Network (CNN) Algorithms, including AlexNet, LeNet, VGGNet, 1DCNN, ResNet, ZF Net, and DenseNet as the basic models. The five architectures with the highest accuracy were then used in the ensemble method, which included ResNet, DenseNet, LeNet, AlexNet, and 1DCNN, with the final results determined by the majority values. The ensemble approach performed with 79.18 % of the final accuracy result. Other architectures include AlexNet 77.1 %, ZF Net 44.50 %, VGG Net 39.30 %, 1DCNN 75.44 %, ResNet 77.34 %, DenseNet 77.32 %, and LeNet 48.58 %. It was discovered that the proposed ensemble model outperformed each algorithm. Finally, when a new bug is discovered, it can be added to the proposed model, which will then determine its priority level.