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Pieces of contextual information suitable for predicting co-changes? An empirical study

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Abstract

Models that predict software artifact co-changes have been proposed to assist developers in altering a software system and they often rely on coupling. However, developers have not yet widely adopted these approaches, presumably because of the high number of false recommendations. In this work, we conjecture that the contextual information related to software changes, which is collected from issues (e.g., issue type and reporter), developers’ communication (e.g., number of issue comments, issue discussants and words in the discussion), and commit metadata (e.g., number of lines added, removed, and modified), improves the accuracy of co-change prediction. We built customized prediction models for each co-change and evaluated the approach on 129 releases from a curated set of 10 Apache Software Foundation projects. Comparing our approach with the widely used association rules as a baseline, we found that contextual information models and association rules provide a similar number of co-change recommendations, but our models achieved a significantly higher F-measure. In particular, we found that contextual information significantly reduces the number of false recommendations compared to the baseline model. We conclude that contextual information is an important source for supporting change prediction and may be used to warn developers when they are about to miss relevant artifacts while performing a software change.

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Notes

  1. Dataset and scripts are available at https://zenodo.org/record/2635857

  2. https://github.com/MetricsGrimoire/Bicho

  3. https://github.com/MetricsGrimoire/CVSAnalY

  4. OpenHub can be accessed at https://www.openhub.net

  5. Example for Derby project: http://igor.pro.br/cochanges/derby.html

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Authors and Affiliations

  1. Departamento de Ciência da Computação, Universidade Tecnológica Federal do Paraná, Paraná, Brazil

    Igor Scaliante Wiese & Reginaldo Ré

  2. PPGI/UTFPR–CP, Universidade Tecnológica Federal do Paraná, Paraná, Brazil

    Rodrigo Takashi Kuroda

  3. School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, USA

    Igor Steinmacher & Marco Aurelio Gerosa

  4. School of Computing, Queen’s University, Kingston, Canada

    Gustavo Ansaldi Oliva

  5. School of Computer Science, University of Adelaide, Adelaide, Australia

    Christoph Treude

Authors
  1. Igor Scaliante Wiese
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  2. Rodrigo Takashi Kuroda
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  3. Igor Steinmacher
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  4. Gustavo Ansaldi Oliva
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  5. Reginaldo Ré
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  6. Christoph Treude
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  7. Marco Aurelio Gerosa
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Correspondence to Igor Scaliante Wiese.

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Wiese, I.S., Kuroda, R.T., Steinmacher, I. et al. Pieces of contextual information suitable for predicting co-changes? An empirical study. Software Qual J 27, 1481–1503 (2019). https://doi.org/10.1007/s11219-019-09456-3

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