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Mining Bug Data

A Practitioner’s Guide

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Recommendation Systems in Software Engineering

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Abstract

Although software systems control many aspects of our daily life world, no system is perfect. Many of our day-to-day experiences with computer programs are related to software bugs. Although software bugs are very unpopular, empirical software engineers and software repository analysts rely on bugs or at least on those bugs that get reported to issue management systems. So what makes data software repository analysts appreciate bug reports? Bug reports are development artifacts that relate to code quality and thus allow us to reason about code quality, and quality is key to reliability, end-users, success, and finally profit. This chapter serves as a hand-on tutorial on how to mine bug reports, relate them to source code, and use the knowledge of bug fix locations to model, estimate, or even predict source code quality. This chapter also discusses risks that should be addressed before one can achieve reliable recommendation systems.

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Notes

  1. 1.

    Replace < version > with the downloaded version number of Mozkito.

  2. 2.

    Please see the Mozkito documentation on how to create such a PersistenceUtil instance.

  3. 3.

    There exist more aggregation strategies. Please see the Mozkito manual for more details.

  4. 4.

    mozkito-issues-<version>-jar-with-dependencies.jar

  5. 5.

    mozkito-bugcount-< version >-jar-with-dependencies.jar

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Acknowledgments

We thank Sascha Just and many anonymous reviewers for their work.

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Authors and Affiliations

  1. Saarland University, Saarbrücken, Germany

    Kim Herzig & Andreas Zeller

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  1. Kim Herzig
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  2. Andreas Zeller
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Correspondence to Kim Herzig .

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Editors and Affiliations

  1. McGill University, Montréal, Québec, Canada

    Martin P. Robillard

  2. University of Hamburg, Hamburg, Germany

    Walid Maalej

  3. University of Calgary, Calgary, Alberta, Canada

    Robert J. Walker

  4. Microsoft Research, Redmond, Washington, USA

    Thomas Zimmermann

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Herzig, K., Zeller, A. (2014). Mining Bug Data. In: Robillard, M., Maalej, W., Walker, R., Zimmermann, T. (eds) Recommendation Systems in Software Engineering. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45135-5_6

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