Abstract
In the field of software development, the main problem is to recognize the security-oriented issues within the reported bugs due to its inacceptable rate to provide the satisfied reliability on customer and software dataset. The objective is to propose a novel machine learning approach for multiclass supervised classification named Bug Severity classification to overcome these challenges with the use of supervised Artificial Neural Network and stacking based Navies Bayes classifier. This proposed approach directly examines the latent and highly descriptive features. Primarily, using the natural language programming approaches bug report text is preprocessed. After then, N gram is employed for extracting features by overcoming data sparsity problems. Further, the supervised Artificial Neural Network extracts the salient feature patterns of the corresponding severity classes. Finally, the stacking-based Navies Bayes classifier is employed for classifying multiple bug severity classes. (C) 2020 Elsevier Ltd. All rights reserved.