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A Public Bug Database of GitHub Projects and Its Application in Bug Prediction

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Book cover Computational Science and Its Applications -- ICCSA 2016 (ICCSA 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9789))

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Abstract

Detecting defects in software systems is an evergreen topic, since there is no real world software without bugs. Many different bug locating algorithms have been presented recently that can help to detect hidden and newly occurred bugs in software. Papers trying to predict the faulty source code elements or code segments in the system always use experience from the past. In most of the cases these studies construct a database for their own purposes and do not make the gathered data publicly available. Public datasets are rare; however, a well constructed dataset could serve as a benchmark test input. Furthermore, open-source software development is rapidly increasing that also gives an opportunity to work with public data.

In this study we selected 15 Java projects from GitHub to construct a public bug database from. We matched the already known and fixed bugs with the corresponding source code elements (classes and files) and calculated a wide set of product metrics on these elements. After creating the desired bug database, we investigated whether the built database is usable for bug prediction. We used 13 machine learning algorithms to address this research question and finally we achieved F-measure values between 0.7 and 0.8. Beside the F-measure values we calculated the bug coverage ratio on every project for every machine learning algorithm. We obtained very high and promising bug coverage values (up to 100 %).

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Notes

  1. 1.

    https://github.com.

  2. 2.

    https://www.sourcemeter.com.

  3. 3.

    http://www.cs.waikato.ac.nz/ml/weka/.

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Acknowledgment

This work was partially supported by the European Union project “REPARA – Reengineering and Enabling Performance And poweR of Applications”, project number: 609666.

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Authors and Affiliations

  1. Department of Software Engineering, University of Szeged, Szeged, Hungary

    Zoltán Tóth, Péter Gyimesi & Rudolf Ferenc

Authors
  1. Zoltán Tóth
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  2. Péter Gyimesi
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  3. Rudolf Ferenc
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Corresponding author

Correspondence to Zoltán Tóth .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  5. Polytechnic University, Bari, Italy

    Carmelo M. Torre

  6. Monash University, Clayton, Victoria, Australia

    David Taniar

  7. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  8. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  9. Beijing Univ. of Posts & Telecomm., Beijing, China

    Shangguang Wang

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Tóth, Z., Gyimesi, P., Ferenc, R. (2016). A Public Bug Database of GitHub Projects and Its Application in Bug Prediction. In: Gervasi, O., et al. Computational Science and Its Applications -- ICCSA 2016. ICCSA 2016. Lecture Notes in Computer Science(), vol 9789. Springer, Cham. https://doi.org/10.1007/978-3-319-42089-9_44

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  • DOI: https://doi.org/10.1007/978-3-319-42089-9_44

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42088-2

  • Online ISBN: 978-3-319-42089-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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