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International Conference on Computational Science and Its Applications

ICCSA 2019: Computational Science and Its Applications – ICCSA 2019 pp 518–532Cite as

A Two-Phase Bug Localization Approach Based on Multi-layer Perceptrons and Distributional Features

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11619))

Abstract

Bug localization is a challenging and time-consuming task of the process of bug fixing and, more in general, of software maintenance. Several approaches have been proposed in the literature which support developers in this task by identifying source code files in which the bug is likely to be located. However, the research on this topic never stopped, looking for new methods providing better accuracy and/or better efficiency. In this paper, we propose a two-phase bug localization approach which leverages multi-layer neural networks and distributional features. First phase locations are obtained thanks to a neural network trained on word embeddings representations of fixed bug reports. The second phase refines bug locations taking into account the number of times source code files co-occur in fixed bug locations. To evaluate the approach, we conducted a large-scale experiment on five open source projects, namely Mozilla, Eclipse, Dolphin, httpd, and gcc. Results show that, thanks to pre-trained word embeddings, we were able to implement a scalable approach with a training running time of few hours on large datasets. Performances are comparable to other existing deep learning approaches.

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Notes

  1. 1.

    https://www.bugzilla.org.

  2. 2.

    https://www.bugzilla.org/docs/4.2/en/html/lifecycle.html.

  3. 3.

    https://nlp.stanford.edu/projects/glove/.

  4. 4.

    https://catalog.ldc.upenn.edu/LDC2011T07.

  5. 5.

    For the implementation of the MLP network we used scikit-learn 0.19.2.

  6. 6.

    also known as the comment at bug reporting time.

References

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Authors and Affiliations

  1. University of Rome Unitelma Sapienza, Rome, Italy

    Damiano Distante & Stefano Faralli

Authors
  1. Damiano Distante
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  2. Stefano Faralli
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Corresponding author

Correspondence to Stefano Faralli .

Editor information

Editors and Affiliations

  1. Covenant University, Ota, Nigeria

    Sanjay Misra

  2. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  3. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  4. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  5. Saint Petersburg State University, Saint Petersburg, Russia

    Vladimir Korkhov

  6. Polytechnic University of Bari, Bari, Italy

    Carmelo Torre

  7. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  8. Monash University, Clayton, VIC, Australia

    David Taniar

  9. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  10. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

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Distante, D., Faralli, S. (2019). A Two-Phase Bug Localization Approach Based on Multi-layer Perceptrons and Distributional Features. In: Misra, S., et al. Computational Science and Its Applications – ICCSA 2019. ICCSA 2019. Lecture Notes in Computer Science(), vol 11619. Springer, Cham. https://doi.org/10.1007/978-3-030-24289-3_38

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  • DOI: https://doi.org/10.1007/978-3-030-24289-3_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-24288-6

  • Online ISBN: 978-3-030-24289-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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