user-statistician VS javadoc-cleanup

The cicirello/user-statistician GitHub Action generates a detailed visual summary of your activity on GitHub in the form of an SVG suitable to display on your GitHub Profile README Although the intended use-case is to generate an SVG image for your GitHub Profile README you can also potentially link to the image from a personal website, or from anywhere else where you'd like to share a summary of your activity on GitHub. The SVG that the action generates includes statistics for the repositories that you own, your contribution statistics (e.g., commits, issues, PRs, etc), as well as the distribution of languages within public repositories that you own The user stats image can be customized, including the colors such as with one of the built-in themes or your own set of custom…

Contributing a language translation mostly involves creating a new JSON file named with the ISO 639-1 two-character code for the language, or for languages that don't have a two-character code, the ISO 639-2 three-character language code. Then within that JSON file, translating all of the string values. You also need to add the locale code to a Python set of supported locales within src/StatConfig.py.

I maintain several GitHub Actions, such as jacoco-badge-generator, generate-sitemap, javadoc-cleanup, and user-statistician. I've also written posts here on DEV about each of these if you'd like more information. GitHub's documentation for GitHub Action developers recommends maintaining a major release tag for the Action so that users can either reference the Action by its specific release tag, such as v1.2.3, or simply by the major release with v1. In fact, it is so commonplace that users will likely assume that your Action supports specifying full version tag or major tag only. Note that some Actions use major release branches (e.g., branch named v1) instead of tags. My intention in this post is not to discuss the advantages/disadvantages of each of these alternative approaches. In the Actions that I maintain, I use major release tags for the simple reason that it is what GitHub's documentation recommends.

The complete workflow for this project is found at: build.yml. The repository itself is:

I use GitHub Actions to automate a variety of things in nearly all of my repositories, such as running a build and tests during pull-requests and pushes, deploying artifacts to Maven Central, etc for my Java libraries, or to PyPI for a couple Python projects, building my personal website with my custom static site generator, among a variety of other tasks. In addition to using GitHub Actions for workflow automation, I also develop and maintain a few Actions (all implemented in Python), including jacoco-badge-generator, user-statistician, javadoc-cleanup, and generate-sitemap.

If you want to generate the equivalent to the above for your own GitHub profile, check out the cicirello/user-statistician GitHub Action.

The cicirello/user-statistician GitHub Action generates a detailed visual summary of your activity on GitHub in the form of an SVG suitable to display on your GitHub Profile README Although the intended use-case is to generate an SVG image for your GitHub Profile README you can also potentially link to the image from a personal website, or from anywhere else where you'd like to share a summary of your activity on GitHub. The SVG that the action generates includes statistics for the repositories that you own, your contribution…

The user-statistician GitHub Action is implemented in Python as a container action. You don't need to know any Docker to contribute, as you won't need to touch the Dockerfile, and the unit tests can run locally with Python alone (the unit tests don't actually query the GitHub API, instead using fake query results). If you know how to add elements to a Python dictionary, then your Python background is sufficient. There is a single Python file that you would need to edit: StatConfig.py. There is a comment within that has an itemized list of what is required to contribute a language translation, and mostly involves adding your translation of the various headings and labels, as well as a translation of the title template, to a couple of Python dictionaries, and adding the language code to a Python set.

Just in time for Halloween, and Hacktoberfest, I recently added a few Halloween themes to the user-statistician GitHub Action. I've posted about the user-statistician GitHub Action before. It generates an SVG with a detailed summary of your activity on GitHub suitable for inclusion in your GitHub Profile README or on a personal website. The intended use-case is to run on a schedule via a GitHub workflow in your GitHub Profile repository (repository with same name as your username). It is implemented in Python as a Container Action, and uses the GitHub CLI to query the GitHub GraphQL API to gather the data. For a more detailed summary of its functionality, see my earlier DEV post as well as other posts in this series:

In this post, I explain how to configure GitHub's Linguist within your repository to enable more accurate and more relevant repository language reporting, with examples from a few of my own repositories. Every repository on GitHub has a chart that shows the distribution of languages detected in the repository. GitHub's Linguist is responsible for detecting the language of each file within your repository, and the reported percentages are based on file sizes. For example, "Java 50%" means that 50% of the total size of all detected files in the repository are Java files. There are also third party tools that display language statistics, such as the user-statistician GitHub Action that I developed and maintain, which includes on an SVG (among other things) a pie chart summarizing the language distribution across all of your public repositories (excluding forks). The language data necessary to generate that language chart comes from GitHub's GraphQL API, which is as it is reported for each of your repositories by Linguist.

javadoc-cleanup - Create mobile-friendly and SEO ready documentation sites by post-processing javadocs in GitHub Actions

A better approach that speeds up runs of your action is to use a base image that includes all of the tools that your action requires. This way, your Dockerfile only requires the steps necessary to copy the source of your action into the container and to set an entrypoint. Here is an example of the Dockerfile of one of my GitHub Actions (note this one doesn't use the containers that are the subject of this post):

I maintain several GitHub Actions, such as jacoco-badge-generator, generate-sitemap, javadoc-cleanup, and user-statistician. I've also written posts here on DEV about each of these if you'd like more information. GitHub's documentation for GitHub Action developers recommends maintaining a major release tag for the Action so that users can either reference the Action by its specific release tag, such as v1.2.3, or simply by the major release with v1. In fact, it is so commonplace that users will likely assume that your Action supports specifying full version tag or major tag only. Note that some Actions use major release branches (e.g., branch named v1) instead of tags. My intention in this post is not to discuss the advantages/disadvantages of each of these alternative approaches. In the Actions that I maintain, I use major release tags for the simple reason that it is what GitHub's documentation recommends.

We now need a way to detect if the results of the above integration tests are correct. The various actions that I maintain produce files (e.g., jacoco-badge-generator produces coverage badges, and generate-sitemap produces an XML sitemap) or edits existing files (e.g., javadoc-cleanup inserts canonical links and a few other things into the head of javadoc pages). In cases like these, I use Python's unittest module to validate the results. In this case, I define unit test cases in tests/integration.py that verify that the files produced by the action are correct. If any of those tests fail, then Python will exit with a non-zero exit code which will cause the workflow to fail.

The next step uses a GitHub Action that I've implemented javadoc-cleanup to insert canonical URLs into the head of each javadoc page. I also use it to insert a referrer policy of strict-origin-when-cross-origin into the head of each javadoc page, as well as links to my project's favicon, and my web monetization pointer. This step is conditional, like the previous step, and only runs on release and workflow_dispatch events.

This post introduces javadoc-cleanup, a GitHub Action that I developed a while ago for post-processing javadoc documentation prior to deploying to a documentation website. I use it in several of my own Java projects to improve the output of javadoc in a few ways. The functionality of javadoc-cleanup includes the following:

There are two primary ways of implementing a GitHub Action: JavaScript Actions and Container Actions. The latter of which enables implementing Actions in any language via a Docker container. My language of choice for implementing GitHub Actions is Python. The purpose of most of these actions is to produce files (e.g., jacoco-badge-generator produces test coverage badges as SVGs, and generate-sitemap produces an XML sitemap) or to edit files in some way (e.g., javadoc-cleanup can insert canonical links and other user-defined elements into the head of javadoc pages). However, all of these also produce workflow step outputs. For example, generate-sitemap has outputs for the number of pages in the sitemap, and the number of pages excluded from the sitemap due to noindex or robots.txt exclusions; and jacoco-badge-generator has workflow step outputs for the coverage and branches coverage percentages if a user had some reason to use those in later steps of their workflow.