72dab1a6d8
* Refactor things to avoid multiple sums and set updates * Store max and min values too for experiences * Store touched files and directories too as part of the commit data * Remove useless default value for files_modified_num * Use f-string instead of string concatenation for feature names * Add more features about experiences (average, maximum, minimum, number of elements) Fixes #370 |
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bugbug | ||
http_service | ||
infra | ||
scripts | ||
tests | ||
.dockerignore | ||
.flake8 | ||
.gitignore | ||
.isort.cfg | ||
.pre-commit-config.yaml | ||
.taskcluster.yml | ||
CODE_OF_CONDUCT.md | ||
CONTRIBUTING.md | ||
LICENSE | ||
MANIFEST.in | ||
README.md | ||
VERSION | ||
comment_level_labeler.py | ||
docker-compose.yml | ||
extra-nlp-requirements.txt | ||
extra-nn-requirements.txt | ||
pytest.ini | ||
requirements.txt | ||
run.py | ||
setup.cfg | ||
setup.py | ||
test-requirements.txt |
README.md
bugbug
Classifiers
-
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.
-
bugtype - The aim of this classifier is to classify bugs according to their type.
Setup
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
.
Usage
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
- Clone https://hg.mozilla.org/mozilla-central/.
- Run
./mach vcs-setup
in the directory where you have cloned mozilla-central. - Enable the pushlog, hgmo and mozext extensions. For example, if you are on Linux, add the following to the extensions section of the
~/.hgrc
file:pushlog = ~/.mozbuild/version-control-tools/hgext/pushlog hgmo = ~/.mozbuild/version-control-tools/hgext/hgmo mozext = ~/.mozbuild/version-control-tools/hgext/mozext firefoxtree = ~/.mozbuild/version-control-tools/hgext/firefoxtree
- 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.
Auto-formatting setup
This project is using pre-commit. Please run pre-commit install
to install the git pre-commit hooks on your clone.
Then every time you will try to commit, it will check that the files are correctly formatted before letting you commit.