JDP

In simple terms; JDP makes creating test result reports (amongst other things) easy.

In less simple terms, JDP is an Extensible, sometimes automated, test/bug review and reporting development environment. The broader aim is to make prototyping arbitrary reporting and inter-tool workflows cheap so that experimentation in this area has a convex payoff.

• JDP may be used as a library in a larger project or as an application/service[1].
• JDP is not a polished product for non-technical users, but you can use it to make that.
• JDP makes data from multiple sources/trackers easily accessible, but it is not a source of truth.
• JDP can post back to trackers; it can automate workflows other than reporting.
• JDP is intended to fit into a CI/CD pipeline or take over unusual sections of a pipeline, it is not intended as a replacement for Jenkins, GoCD, GitlabCI, etc.

Initially JDP is targeted at SUSE's QA Kernel & Networking team's requirements, however it is a general purpose tool at its core. It could be used with any type of data for most any type of workflow or reporting.

Install

The goal is to do this in a single command, but for now it takes a few more.

Docker

You can install using Docker by doing the following from the directory where you cloned this repo. This is probably the easiest way if you just want to quickly try it out.

docker build -t jdp:latest -f install/Dockerfile .

Or you can substitute the build command for the following which will get a pre-built image from hub.docker.com (it may not be up to date).

docker pull suserichiejp/jdp:latest

Then you can inject the access details for the data cache server if you have them. Using the data cache can save a lot of time.

docker build -t jdp:latest -f install/Dockerfile-slave \
             --build-arg REDIS_MASTER_HOST=ip-or-name \
             --build-arg REDIS_MASTER_AUTH=password .

Then run it

docker run -it -p 8889:8889 jdp:latest

With a bit of luck you will see a message from Jupyter describing what to do next. The Docker image also contains two volumes which you may mount. See the Dockerfile for more info.

You can use the Docker image for developing JDP itself by mounting the src volume. However this is probably not a good long term solution.

Other

You can use install/Dockerfile as a guide. Also check conf/*.toml.

You can run JDP directly from the git checkout. Just install the deps listed in the Dockerfile and modify the conf files (which should include there own documentation).

Usage

With Jupyter

If you are using the Docker image then just browse to localhost:8889. If not then start Jupyter yourself.

Open either the notebooks/Report-DataFrames.ipynb or notebooks/Propagate Bug Tags.ipynb Jupyter notebooks which are (hopefully) self documenting. I have only tested them with Jupyter itself, but there are fancier alternatives such as JupyterLab and, of course, Emacs.

Other

You can also use the library from a Julia REPL or another project. For example in a julia REPL you could run

include("src/init.jl")

Also the run directory contains scripts which are intended to automate various tasks. These can be executed with Julia in a similar way to julia run/all.jl.

Automation

JDP is automated using SUSE's internal Gitlab CI instance. Which automates building and testing the containers as well as deployment and the execution of various scripts/services. See install/gitlab-ci.*.

Documentation

Further documentation can be found at richiejp.github.io/jdp or rpalethorpe.io.suse.de/jdp

You can also find documentation at the Julia REPL by typing ? followed by an identifier or in a notebook you can type @doc identifier in a code cell.

The following image may give you some intuition for what JDP is.

Contributors

Created and maintained by Richard Palethorpe (rpalethorpe@suse.com). Sebastian Chlad (schlad@suse.com) is mainly responsible for it being a serious (I hope) project.

Cyril has been asking for a result difference view and matrix for years.

Ideas and feedback

Because it is not obvious who has contributed non-code or documentation changes I will try to make a list. Please let me know if I have missed you out or want to be removed.

• Sebastian Chlad
• Cyril Chrubis
• Yong Sun
• Anton Smorodskyi
• Sergio Lindo
• Petr Vorel
• Oliver Kurz
• Clemans Famulla-Conrad
• Jose Lausuch
• Petr Cervinka

Code and documentation

See the github/lab stats.