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sils

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FWIW the default thing is actually https://i.imgur.com/JO0nXL7.png

This table is our aggregated view that gets triggered if there's a lot of comments that would spam the PR.

The code review functionality is completely open source at code.gitmate.io FWIW.

We grew from the whole review thing into automation for PRs (setting lots of labels for PRs automatically, managing review queues; it's all in GitMate) and eventually started with issues. Our next step is to split the review and issues because this is becoming very hard to communicate marketing wise ;)

coala always leaves you in control, you can tell it to e.g. show a patch before applying. If you explicitly configure it you can set it to automatically apply patches that are coming from certain analysis routines.

So ATM the only way to get more info about a bear is by querying coala for it: `coala -B -b=AlexBear`

We have a project in planning stage to make a browsable webpage generated from the per bear documentation, we will also look a lot at the configurability and documentation of the individual bears for the next release.

Indeed. coala is more of a framework that allows you to plug in any analysis routine. However, it also provides means to create new code analysis very easily and combine arbitrary routines.

So yes, there are some very simple generic algorithms (line length checking, spacing corrections, we have things like variable name checking in the works which only need a few lines "language definition") and you can combine them with - possibly wrapped or not - language dependent algorithms easily.

This is useful for the user because he doesn't have to learn and configure tons of tools and he has some basic functionality for every language - even the ones he writes tomorrow.

It's also useful for people writing static code analysis because they can just write their algorithm and be done with it - why should everybody rewrite a CLI interface, editor plugins and so on?

I wrote a thesis about code clone detection (another algorithm that is basically generic and would need only a relatively small parsing part per language, coala allows me to modularize meanfully into parsing and language independent AST processing) and it just took me a few functions that actually performed the analysis. People can now use it easily which usually isn't true for research programs and I had even less work than I would have if I had written it without coala.