Improved Framework for Bug Severity Classification using N-gram Features with Convolution Neural Network
Sarbjeet Kaur1, Maitreyee Dutta2
1Sarbjeet Kaur, Department of Computer Science, NITTTR, Chandigarh, India.
2Maitreyee Dutta, Department of Information Management and Co-ordination Unit.
Manuscript received on 15 August 2019. | Revised Manuscript received on 25 August 2019. | Manuscript published on 30 September 2019. | PP: 1190-1196 | Volume-8 Issue-3 September 2019 | Retrieval Number: C4292098319/19©BEIESP | DOI: 10.35940/ijrte.C4292.098319
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Bug Severity, Bug Prediction, Bi-grams
Scope of the Article: Classification