Guide to Write a Bear

Welcome. This document presents information on how to write a bear for coala. It assumes you know how to use coala. If not please read our main tutorial!

The sample sources for this tutorial lie at our coala-tutorial repository, go clone it with:

git clone https://github.com/coala-analyzer/coala-tutorial.git

All paths and commands given here are meant to be executed from the root directory of the coala-tutorial repository.

What is a bear?

A bear is meant to do some analysis on source code. The source code will be provided by coala so the bear doesn’t have to care where it comes from or where it goes.

A bear can communicate with the user via two ways:

  • Via log messages
  • Via results

Log messages will be logged according to the users settings and are usually used if something goes wrong. However you can use debug for providing development related debug information since it will not be shown to the user by default. If error/failure messages are used, the bear is expected not to continue analysis.

A Hello World Bear

Below is the code given for a simple bear that sends a debug message for each file:

from coalib.bears.LocalBear import LocalBear


class HelloWorldBear(LocalBear):
    def run(self,
            filename,
            file):
        self.debug("Hello World! Checking file", filename, ".")

This bear is stored at ./bears/HelloWorldBear

In order to let coala execute this bear you need to let coala know where to find it. We can do that with the -d (--bear-dirs) argument:

coala -f src/*.c -d bears -b HelloWorldBear -L DEBUG

You should now see the debug message for our sample file.

The Bear class also supports warn and err.

Communicating with the User

Now we can send messages through the queue, we can do the real work. Lets say:

  • We want some information from the user (e.g. the tab width if we rely on indentation)
  • We’ve got some useful information for the user and want to show it to him. This might be some issue with his code or just an information like the number of lines.

So let’s extend our HelloWorldBear a bit, I’ve named the new bear with the creative name CommunicationBear:

from coalib.bears.LocalBear import LocalBear
from coalib.results.Result import Result


class CommunicationBear(LocalBear):

    def run(self,
            filename,
            file,
            user_input: str):
        """
        Communicates with the user.

        :param user_input: Arbitrary user input.
        """
        self.debug("Got '{ui}' as user input of type {type}.".format(
            ui=user_input,
            type=type(user_input)))

        return [Result.from_values(message="A hello world result.",
                                   origin=self,
                                   file=filename)]

Try executing it:

coala -f=src/\*.c -d=bears -b=CommunicationBear -L=DEBUG

Hey, we’ll get asked for the user_input! Wasn’t that easy? Go ahead, enter something and observe the output.

So, what did coala do here?

First, coala looked at the parameters of the run method and found that we need some value named user_input. Then it parsed our documentation comment and found a description for the parameter which was shown to us to help us choose the right value. After the needed values are provided, coala converts us the value into a string because we’ve provided the str annotation for this parameter. If no annotation is given or the value isn’t convertible into the desired data type, you will get a coalib.settings.Setting.Setting.

Your docstring can also be used to tell the user what exactly your bear does.

Try executing

coala -d bears -b CommunicationBear --show-bears

This will show the user a bunch of information related to the bear like: - A description of what the bear does - The sections which uses it - The settings it uses (optional and required)

What Data Types are Supported?

The Setting does support some very basic types:

  • String (str)
  • Float (float)
  • Int (int)
  • Boolean (bool, will accept values like true, yes, yeah, no, nope, false)
  • List of strings (list, values will be split by comma)
  • Dict of strings (dict, values will be split by comma and colon)

If you need another type, you can write the conversion function yourself and use this function as the annotation. We’ve provided a few advanced conversions for you:

  • coalib.settings.Setting.path, converts to an absolute file path relative to the file/command where the setting was set
  • coalib.settings.Setting.path_list, converts to a list of absolute file paths relative to the file/command where the setting was set
  • coalib.settings.Setting.typed_list(typ), converts to a list and applies the given conversion (typ) to each element.
  • coalib.settings.Setting.typed_ordered_dict(key_type, value_type, default), converts to a dict while applying the key_type conversion to all keys, the value_type conversion to all values and uses the default value for all unset keys. Use typed_dict if the order is irrelevant for you.

Results

In the end we’ve got a result. If a file is provided, coala will show the file, if a line is provided, coala will also show a few lines before the affecting line. There are a few parameters to the Result constructor, so you can e.g. create a result that proposes a code change to the user. If the user likes it, coala will apply it automatically - you don’t need to care.

Your function needs to return an iterable of Result objects: that means you can either return a list of Result objects or simply yield them and write the method as a generator.

Note

We are currently planning to simplify Bears for bear writers and us. In order to make your Bear future proof, we recommend writing your method in generator style.

Don’t worry: in order to migrate your Bears to our new API, you will likely only need to change two lines of code. For more information about how bears will look in the future, please read up on https://github.com/coala-analyzer/coala/issues/725 or ask us on https://gitter.im/coala-analyzer/coala.