Recomp Metrics

The following instructions are for unix-based systems.

Code Organization

The code to generate the data is in the main branch, located in src/recomp-metrics.

The data is in the gh-pages branch, located in data/RC/snapshot.json and data/RC/progress.json.

Directory Structure

To update the data, both the main and gh-pages branches of this repository need to be available as separate directories. The easiest way to achieve that is to add the gh-pages branch as a separate worktree under your clone of this repo:

git worktree add gh-pages origin/gh-pages

You will also need a git checkout of the mozilla-firefox/firefox repository:

git clone [email protected]:mozilla-firefox/firefox.git mozilla-unified

Following these steps results in the following directory tree, but this is not required as the directory names and paths are arbitrary.

.
├── recomp-metrics   // a checkout of the main branch
|   └── gh-pages     // a checkout of the gh-pages branch
└── mozilla-unified  // a mercurial checkout of mozilla-unified

Generating Data

Aggregating data for RC can be done by statically analyzing the repository.

python3 src/recomp-metrics/aggregate.py -m RC --mc ../mozilla-unified --gh-pages-data gh-pages/data --use-current-revision --git

Committing the Data

Once you've generated new data for RC, you may want to serve the static site locally to view the update yourself. For example, you can run python3 -m http.server 8000 from the folder with the gh-pages clone.

Ultimately, you will need to add the changes as a commit on the gh-pages branch and push them to the repository.

modified:   data/RC/progress.json
modified:   data/RC/snapshot.json

Running Everything Locally

We often make changes to the main and gh-pages branches simultaneously. To get all the pieces working together locally, follow these steps:

1. From the main branch checkout whatever feature branch you are working on or reviewing
2. Run the command to generate data
3. cd into gh-pages
4. Switch to the gh-pages branch you are working on or reviewing
5. Run python3 -m http.server 8000 to start a local server