ranpox / bugbug

Platform for Bugzilla Machine Learning projects

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Discussions happen in #bugbug on irc.mozilla.org.

  1. Issues marked as good first issue are self-contained enough that a contributor should be able to work on them.
  2. Issues marked as reserved-for-beginners are reserved for people who have never contributed to the project before.
  3. Issues are to be considered as not assigned, until there is a PR linked to them.


  • bug vs feature - Bugs on Bugzilla aren't always bugs. Sometimes they are feature requests, refactorings, and so on. The aim of this classifier is to distinguish between bugs that are actually bugs and bugs that aren't. The dataset currently contains 2110 bugs, the accuracy of the current classifier is ~93% (precision ~95%, recall ~94%).

  • defect vs feature vs task - Extension of the previous classifier to detect differences also between feature requests and development tasks.

  • component - The aim of this classifier is to assign product/component to (untriaged) bugs.

  • regression vs non-regression - Bugzilla has a regression keyword to identify bugs that are regressions. Unfortunately it isn't used consistently. The aim of this classifier is to detect bugs that are regressions.

  • tracking - The aim of this classifier is to detect bugs to track.

  • uplift - The aim of this classifier is to detect bugs for which uplift should be approved and bugs for which uplift should not be approved.

  • devdocneeded - The aim of this classifier is to detect bugs which should be documented for developers.

  • qaneeded - The aim of this classifier is to detect bugs that would need QA verification.


Run pip install -r requirements.txt and pip install -r test-requirements.txt

If you update the bugs database, run xz -v9 -k data/bugs.json. If you update the commits database, run xz -v9 -k data/commits.json.


Run the run.py script to perform training / classification. The first time run.py is executed, the --train argument should be used to automatically download databases containing bugs and commits data.

Running the repository mining script

  1. Clone https://hg.mozilla.org/mozilla-central/.
  2. Run ./mach vcs-setup in the directory where you have cloned mozilla-central.
  3. Run the repository.py script, with the only argument being the path to the mozilla-central repository.

Note: the script will take a long time to run (on my laptop more than 7 hours). If you want to test a simple change and you don't intend to actually mine the data, you can modify the repository.py script to limit the number of analyzed commits. Simply add limit=1024 to the call to the log command.

Structure of the project

  • bugbug/labels contains manually collected labels;
  • bugbug/db.py is an implementation of a really simple JSON database;
  • bugbug/bugzilla.py contains the functions to retrieve bugs from the Bugzilla tracking system;
  • bugbug/repository.py contains the functions to mine data from the mozilla-central (Firefox) repository;
  • bugbug/bug_features.py contains functions to extract features from bug/commit data;
  • bugbug/model.py contains the base class that all models derive from;
  • bugbug/models contains implementations of specific models;
  • bugbug/nn.py contains utility functions to include Keras models into a scikit-learn pipeline;
  • bugbug/utils.py contains misc utility functions;
  • bugbug/nlp contains utility functions for NLP;
  • bugbug/labels.py contains utility functions for handling labels;
  • bugbug/bug_snapshot.py contains a module to play back the history of a bug.
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Platform for Bugzilla Machine Learning projects

License:Mozilla Public License 2.0


Language:Python 100.0%