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

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Intelligent Systems Design and Applications (ISDA 2018 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((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.

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Author information

Authors and Affiliations

  1. Delhi College of Arts and Commerce, University of Delhi, Delhi, India

    Madhu Kumari & V. B. Singh

Authors
  1. Madhu Kumari
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  2. V. B. Singh
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Corresponding author

Correspondence to Madhu Kumari .

Editor information

Editors and Affiliations

  1. Machine Intelligence Research Labs, Auburn, WA, USA

    Ajith Abraham

  2. School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

    Aswani Kumar Cherukuri

  3. Tijuana Institute of Technology, Tijuana, Mexico

    Patricia Melin

  4. Machine Intelligence Research Labs, Auburn, WA, USA

    Niketa Gandhi

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Kumari, M., Singh, V.B. (2020). An Improved Classifier Based on Entropy and Deep Learning for Bug Priority Prediction. In: Abraham, A., Cherukuri, A.K., Melin, P., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2018 2018. Advances in Intelligent Systems and Computing, vol 940. Springer, Cham. https://doi.org/10.1007/978-3-030-16657-1_53

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