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Automated topic naming

Supporting cross-project analysis of software maintenance activities

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Empirical Software Engineering Aims and scope Submit manuscript

Abstract

Software repositories provide a deluge of software artifacts to analyze. Researchers have attempted to summarize, categorize, and relate these artifacts by using semi-unsupervised machine-learning algorithms, such as Latent Dirichlet Allocation (LDA). LDA is used for concept and topic analysis to suggest candidate word-lists or topics that describe and relate software artifacts. However, these word-lists and topics are difficult to interpret in the absence of meaningful summary labels. Current attempts to interpret topics assume manual labelling and do not use domain-specific knowledge to improve, contextualize, or describe results for the developers. We propose a solution: automated labelled topic extraction. Topics are extracted using LDA from commit-log comments recovered from source control systems. These topics are given labels from a generalizable cross-project taxonomy, consisting of non-functional requirements. Our approach was evaluated with experiments and case studies on three large-scale Relational Database Management System (RDBMS) projects: MySQL, PostgreSQL and MaxDB. The case studies show that labelled topic extraction can produce appropriate, context-sensitive labels that are relevant to these projects, and provide fresh insight into their evolving software development activities.

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Notes

  1. Generated using David A. Wheeler’s SLOCCount, http://dwheeler.com/sloccount.

  2. http://www.sdn.sap.com/irj/sdn/maxdb

  3. http://www.postgresql.org/docs/7.3/static/

  4. NLTK: http://www.nltk.org/.

  5. For our word lists visit http://softwareprocess.es/nomen/.

  6. Since the MySQL and MaxDB data had poor records for developer ids, we focused on PostgreSQL.

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Authors and Affiliations

  1. Dept. of Computing Science, University of Alberta, Edmonton, AB, Canada

    Abram Hindle

  2. Dept. of Computer Science, University of British Columbia, Vancouver, BC, Canada

    Neil A. Ernst

  3. David Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, Canada

    Michael W. Godfrey

  4. Dept. Information Eng. and Computer Science, University of Trento, Trento, Italy

    John Mylopoulos

Authors
  1. Abram Hindle
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  2. Neil A. Ernst
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  3. Michael W. Godfrey
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  4. John Mylopoulos
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Corresponding author

Correspondence to Abram Hindle.

Additional information

Editors: Tao Xie, Thomas Zimmermann and Arie van Deursen

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Hindle, A., Ernst, N.A., Godfrey, M.W. et al. Automated topic naming. Empir Software Eng 18, 1125–1155 (2013). https://doi.org/10.1007/s10664-012-9209-9

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