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An Improved Classifier Based on Entropy and Deep Learning for Bug Priority Prediction

  • Madhu KumariEmail author
  • V. B. Singh
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 940)

Abstract

The bug reports are reported at a faster rate, resulting in uncertainties and irregularities in the bug reporting process. The noise and uncertainty also generated due to increasing enormous size of the bugs to the bug tracking system. In order to build a better classifier, we need to take care of these uncertainties and irregularity. In this paper, we built classifiers based on machine learning techniques Naïve Bayes (NB) and Deep Learning (DL) using entropy based measures for bug priority prediction. We have considered severity, summary weight and entropy attribute to predict the bug priority. The experimental analysis is conducted on eight products of an open source project OpenOffice. We have considered the performance measures, namely accuracy, precision, recall and f-measure to compare the proposed approach. We observed that the attribute entropy has improved the performance of classifier in both the cases NB and DL. DL with entropy is performing better than NB with entropy.

Keywords

Deep learning Machine learning Bug priority Bug repositories Summary weight Entropy 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Delhi College of Arts and CommerceUniversity of DelhiDelhiIndia

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