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Software Mining Studies: Goals, Approaches, Artifacts, and Replicability

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Software Engineering (LASER 2013, LASER 2014)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 8987))

Abstract

The mining of software archives has enabled new ways for increasing the productivity in software development: Analyzing software quality, mining project evolution, investigating change patterns and evolution trends, mining models for development processes, developing methods of integrating mined data from various historical sources, or analyzing natural language artifacts in software repositories, are examples of research topics. Software repositories include various data, ranging from source control systems, issue tracking systems, artifact repositories such as requirements, design and architectural documentation, to archived communication between project members. Practitioners and researchers have recognized the potential of mining these sources to support the maintenance of software, to improve their design or architecture, and to empirically validate development techniques or processes. We revisited software mining studies that were published in recent years in the top venues of software engineering, such as ICSE, ESEC/FSE, and MSR. In analyzing these software mining studies, we highlight different viewpoints: pursued goals, state-of-the-art approaches, mined artifacts, and study replicability. To analyze the mining artifacts, we (lexically) analyzed research papers of more than a decade. In terms of replicability we looked at existing work in the field in mining approaches, tools, and platforms. We address issues of replicability and reproducibility to shed light onto challenges for large-scale mining studies that would enable a stronger conclusion stability.

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Notes

  1. 1.

    http://msrconf.org.

  2. 2.

    http://www.ifi.uzh.ch/seal/events/msa2010.html.

  3. 3.

    http://www.ifi.uzh.ch/seal/events/ASDS-2013.html.

  4. 4.

    http://msrcanada.org/msrvision2020/.

  5. 5.

    http://MSRconf.org.

  6. 6.

    http://www.se-on.org/.

  7. 7.

    http://findbugs.sourceforge.net/.

  8. 8.

    http://openscience.us/repo/.

  9. 9.

    We used the java PDF library Apache PDFBox, https://pdfbox.apache.org/.

  10. 10.

    http://jmlr.csail.mit.edu/papers/volume5/lewis04a/a11-smart-stop-list/english.stop.

  11. 11.

    www.promisedata.org.

  12. 12.

    www.krugle.org.

  13. 13.

    www.openhub.net.

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Acknowledgements

This work was partially funded by the German Federal Ministry of Education and Research (BMBF) within the Software Campus projects KaVE and Eko, both grant no. 01IS12054. The views and opinions expressed in this article are those of the authors and do not necessarily reflect the official policy or position of the funding agency.

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  1. TU Darmstadt, Darmstadt, Germany

    Sven Amann

  2. Alpen-Adria University Klagenfurt, Klagenfurt, Austria

    Stefanie Beyer

  3. University of Zurich, Zurich, Switzerland

    Katja Kevic & Harald Gall

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  1. Sven Amann
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Correspondence to Sven Amann .

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  1. ETH Zurich , Zürich, Switzerland

    Bertrand Meyer

  2. ETH Zurich , Zürich, Switzerland

    Martin Nordio

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Amann, S., Beyer, S., Kevic, K., Gall, H. (2015). Software Mining Studies: Goals, Approaches, Artifacts, and Replicability. In: Meyer, B., Nordio, M. (eds) Software Engineering. LASER LASER 2013 2014. Lecture Notes in Computer Science(), vol 8987. Springer, Cham. https://doi.org/10.1007/978-3-319-28406-4_5

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