Open AccessImproved Framework for Bug Severity Classification using N-gram Features with Convolution Neural Network
Author(s) -
Sarbjeet Kaur*,
Maitreyee Dutta
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.c4292.098319
Subject(s) - seriousness , computer science , convolution (computer science) , machine learning , process (computing) , artificial intelligence , artificial neural network , feature (linguistics) , pattern recognition (psychology) , natural language processing , programming language , linguistics , philosophy , political science , law
Foreseeing the seriousness/severity of bugs has been established in former research study in order to recover triaging and the process of bug resolution. Therefore, numerous prediction/classification methodologies were developed throughout the years to give an automated reasoning over the seriousness classes. Seriousness or severity is a significant trait of a bug that chooses how rapidly it ought to be measured. It causes designers to comprehend significant bugs on schedule. Though, manual evaluation of severity is a dreary activity and could be off base. This paper comprises of using the text/content mining together along with the use feature selection and bi-grams to improve the order of bugs in six classes. In the proposed methodology the features are refined by the use of convolution layers. Here, the process of convolution-based refining indicates mapping of the features utilizing non-linear methods of all the classes as compared to the existing methodologies.