How is the Linux Kernel development going ? Duks is a [work in progress] data visualization tool to help with that.

duks.rcpassos.me

To run DUKS in development, it is recommended to use the dev-compose.yaml file. This starts the server with the Backend (that also serves the Frontend).

podman-compose -f dev-compose.yaml up

Obtain the sample data files in the archived replication package.

The file structure

├── data    -> where the data files should be stored
├── nginx    -> configurations for nginx (for production)
├── scripts   -> scripts used to generate the dataset
└── server   -> backend source code
    └── static -> static Frontend files

Bootstrapping this from zero requires a few external resources.

Requirements:

• Local compressed Software Heritage Graph (at least “History and hosting” graph: 1.4TiB for 2024-12-06) : https://docs.softwareheritage.org/devel/swh-export/graph/index.html
• Linux kernel mainline tree
• Software heritage GRPC server: https://docs.softwareheritage.org/devel/swh-graph/grpc-api.html#starting-the-server

This repository is organized to use uv as its python dependencies, this document will use it. Run scripts from the project root directory (as it will create a ./data/ folder relative to the scripts). Other options should work.

1. run the GRPC server with the local graph, following the upstream guide. Example: swh-graph-grpc-serve --bind 0.0.0.0:50091 /media/research/2025-05-18-history-hosting/graph
2. run uv run scripts/grpc_script.py to execute the BFS on the graph. This script will look for the last commit in the DEFAULT_BRANCH for the chosen KERNEL_TREE. Defaults to the master in the mainline kernel. It is also possible to pre-load commits manually. Must set the GRAPH_GRPC_SERVER and INITIAL_NODE, if non-default port or commit would be used.
3. run the uv run scripts/enrich_from_git.py script, pointing to the mainline branch path. This will load data that is unavailable in the graph. Tags can be loaded from the previous step, or loaded here if LOAD_TAGS_FROM_REPO is set. Example: KERNEL_PATH=/media/research/linux uv run scripts/enrich_from_git.py
4. run the scripts/get_official_kernel_maintainers.py to read the contents of the maintainers file in all its changes. This step is independent from others, besides step 5. Example: KERNEL_PATH=/media/research/linux uv run scripts/get_official_kernel_maintainers.py
5. run the [scripts/stitch_data_into_final_payload.py] to get the daily output (with some calculation) and with all files in a single v

Run either podman-compose -f dev-compose.yaml up for development or podman-compose up for production build

Papers related to this software:

• Streamlining Analyses on the Linux Kernel with DUKS
• DUKS: visualizações e análises unificadas para o Kernel Linux 🇧🇷

PASSOS, Rafael; PILONE, Arthur; TADOKORO, David; MEIRELLES, Paulo