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.
CITATION STYLE
Kaur, S., & Dutta, M. (2019). Improved framework for bug severity classification using n-gram features with convolution neural network. International Journal of Recent Technology and Engineering, 8(3), 1190–1196. https://doi.org/10.35940/ijrte.C4292.098319
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