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Bug Priority Assessment in Cross-Project Context Using Entropy-Based Measure

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Advances in Machine Learning and Computational Intelligence

Part of the book series: Algorithms for Intelligent Systems ((AIS))

Abstract

Software users report bugs on the bug tracking system in a distributed environment with different levels of understanding and knowledge about the software. As a result of this, software bug repository data are increasing day by day with noise and uncertainty in it. The noise and uncertainty present in bug summary need to be handled, so that it should not affect the performance of learning strategies for different bug attributes and fix time predictions. Bug prioritization is a process of deciding the sequence of bugs to be fixed. Wrong bug prioritization results in unresolved important bugs with delayed release of the software, thus affecting the quality and evolution of the software. Bug priority prediction requires historical data for the training of the classifiers. However, such historical data is not always available in practice for all the software. In such circumstances, designing prediction models with training data from other projects is the solution. This process of bug priority prediction using training and testing bug data from two different projects is called cross-project bug priority prediction. We have used Shannon entropy to measure the uncertainty in bug summary in addition to bug severity and summary weight for bug priority prediction. In this paper, we have proposed different machine learning classifiers to predict the priority of a reported bug in cross-project context by handling the uncertainty. Results show performance improvement for proposed entropy-based cross-project bug priority prediction over existing summary-based cross-project bug priority prediction for a newly coming bug report.

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Authors and Affiliations

  1. Swami Shraddhanand College, University of Delhi, Delhi, India

    Meera Sharma

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

    Madhu Kumari & V. B. Singh

Authors
  1. Meera Sharma
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  2. Madhu Kumari
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  3. V. B. Singh
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Corresponding author

Correspondence to V. B. Singh .

Editor information

Editors and Affiliations

  1. School of Computer Science and Engineering, SOA University, Bhubaneswar, Odisha, India

    Srikanta Patnaik

  2. Simulation and Modelling, School of Science and Technology, Middlesex University, London, UK

    **n-She Yang

  3. Department of Computer Science and Engineering, Oakland University, Rochester, MI, USA

    Ishwar K. Sethi

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Sharma, M., Kumari, M., Singh, V.B. (2021). Bug Priority Assessment in Cross-Project Context Using Entropy-Based Measure. In: Patnaik, S., Yang, XS., Sethi, I. (eds) Advances in Machine Learning and Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-5243-4_10

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  • DOI: https://doi.org/10.1007/978-981-15-5243-4_10

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-5242-7

  • Online ISBN: 978-981-15-5243-4

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