Google Code In Task Advanced asciinema
This is a coala GCI repeatable task.
If you use windows, this task may get very complicated. We recommend to start with something else or to get a linux system for this task if possible.
This is a sequel to the simple asciinema task.
Create an asciinema for a coala bear showing its behaviour and greatest features properly.
You will have to setup and install coala so that you can run code analysis with coala. You will pick a coala bear (a "bear" is a code analysis routine in coala), run it on sample code and publish an asciinema.
This activity requires submitting your a small change to the coala/coala-bears repository as a Pull Request, and fixing the code until it is merged by the coala developers.
This task assists participants to learn about running code analysis and making an asciinema, thus documenting how it works.
They will also learn about working with issues and pull requests in an open source project.
To achieve this, you will have to make up on your own, how you can show off the best features of the bear you have selected.
coala provides a lot of static analysis functionality.
It is very useful if users can see it in action before having to install coala on their own.
You can select any bear which has an asciinema but you don't think it is satisfactory. You will find all the bears in the coala-bears repository - however it is way easier to simply browse their documentation at https://coala.io/languages.
https://github.com/coala/coala-bears/search?q=asciinema+[lintername]
If you can find an asciinema issue or pull request for the bear that you have found, check if someone is already working on that asciinema.
If someone is working on it, check his thoughts about improving the bear, consider adding them to the issue. Go back to the previous section.
If nobody is working on it, skip the next section.
- Click on new issue
- Title: "BearName: Add advanced asciinema"
- Add and modify the following template body for the issue:
The coala bear [BearName] does not have a proper asciinema.
<Describe how you plan to improve it here.>
You probably already know how to get an assignment.
You already know those steps: Make your asciinema, make a Pull Request and get it merged.
The mentors will check that your Pull Request was merged, and verify that you followed the steps above.
You may be asked to redo the process if you deviated from the steps above.
There is no follow up task on this one.
coala | bash | lint | static-analysis | github | circleci | appveyorci | continuous-integration |
coala