Bugbug aims at leveraging machine learning techniques to help with bug and quality management, and other software engineering tasks (such as test selection and defect prediction).
Chat with us in the bugbug Matrix room.
More information on the Mozilla Hacks blog:
assignee - The aim of this classifier is to suggest an appropriate assignee for a bug.
backout - The aim of this classifier is to detect patches that might be more likely to be backed-out (because of build or test failures). It could be used for test prioritization/scheduling purposes.
bugtype - The aim of this classifier is to classify bugs according to their type. The labels are gathered automatically from bugs: right now they are "crash/memory/performance/security". The plan is to add more types after manual labeling.
component - The aim of this classifier is to assign product/component to (untriaged) bugs.
defect vs enhancement vs task - Extension of the defect classifier to detect differences also between feature requests and development tasks.
defect - 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%).
devdocneeded - The aim of this classifier is to detect bugs which should be documented for developers.
duplicate - The aim of this classifier is to detect duplicate bugs.
needsdiagnosis - The aim of this classifier is to detect issues that are likely invalid and don't need to be diagnosed for webcompat use case.
qaneeded - The aim of this classifier is to detect bugs that would need QA verification.
regression vs non-regression - Bugzilla has a
regressionkeyword to identify bugs that are regressions. Unfortunately it isn't used consistently. The aim of this classifier is to detect bugs that are regressions.
regressionrange - The aim of this classifier is to detect regression bugs that have a regression range vs those that don't.
regressor - The aim of this classifier is to detect patches which are more likely to cause regressions. It could be used to make riskier patches undergo more scrutiny.
spam - The aim of this classifier is to detect bugs which are spam.
stepstoreproduce - The aim of this classifier is to detect bugs that have steps to reproduce vs those that don't.
testfailure - The aim of this classifier is to detect patches that might be more likely to cause test failures.
testselect - The aim of this classifier is to select relevant tests to run for a given patch.
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.
Setup and Prerequisites
Install the Python dependencies:
pip3 install -r requirements.txt
You may also need
pip install -r test-requirements.txt. Depending on the parts of bugbug you want to run, you might need to install dependencies from other requirement files (find them with
find . -name "*requirements*").
Currently, Python 3.7+ is required. You can double check the version we use by looking at setup.py.
sudo apt-get -t experimental install libgit2-dev
This project is using pre-commit. Please run
pre-commit install to install the git pre-commit hooks on your clone.
Every time you will try to commit, pre-commit will run checks on your files to make sure they follow our style standards and they aren't affected by some simple issues. If the checks fail, pre-commit won't let you commit.
trainer.py script with the command
python -m scripts.trainer (with
--help to see the required and optional arguments of the command) to perform training (warning this takes 30min+).
To use a model to classify a given bug, you can run
python -m scripts.bug_classifier MODEL_NAME --bug-id ID_OF_A_BUG_FROM_BUGZILLA. N.B.: If you run the classifier script without training a model first, it will automatically download an already trained model.
Example for the "defect" model
training To train the model for mode
python3 -m scripts.trainer defect
testing To use the model to classify a given bug, you can run
python -m scripts.bug_classifier defect --bug-id ID_OF_A_BUG_FROM_BUGZILLA.
Running the repository mining script
Note: This section is only necessary if you want to perform changes to the repository mining script. Otherwise, you can simply use the commits data we generate automatically.
- Clone https://hg.mozilla.org/mozilla-central/.
./mach vcs-setupin the directory where you have cloned mozilla-central.
- Enable the extensions mentioned in infra/hgrc. For example, if you are on Linux, you can add
firefoxtreeto the extensions section of the
firefoxtree = ~/.mozbuild/version-control-tools/hgext/firefoxtree
- Run the
repository.pyscript, with the only argument being the path to the mozilla-central repository.
Note: If you run into problems, it's possible the version of Mercurial you are using is not supported. Check the Docker definition at infra/dockerfile.commit_retrieval to see what we are using in production.
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
Structure of the project
bugbug/labelscontains manually collected labels;
bugbug/db.pyis an implementation of a really simple JSON database;
bugbug/bugzilla.pycontains the functions to retrieve bugs from the Bugzilla tracking system;
bugbug/repository.pycontains the functions to mine data from the mozilla-central (Firefox) repository;
bugbug/bug_features.pycontains functions to extract features from bug/commit data;
bugbug/model.pycontains the base class that all models derive from;
bugbug/modelscontains implementations of specific models;
bugbug/nn.pycontains utility functions to include Keras models into a scikit-learn pipeline;
bugbug/utils.pycontains misc utility functions;
bugbug/nlpcontains utility functions for NLP;
bugbug/labels.pycontains utility functions for handling labels;
bugbug/bug_snapshot.pycontains a module to play back the history of a bug;
bugbug/github.pycontains functions to retrieve issues from GitHub for a specified owner/repository.
Using bugbug for non-Mozilla projects
Bugbug is focussing on Mozilla use-cases for Firefox, Bugzilla and GitHub. However, we will be happy to accept pull requests adding support for other projects or bug trackers.