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With source{d} Lookout, we’re introducing a service for assisted code review, that allows running custom code analyzers on pull requests.

Jump to the Quickstart section to start using it!

Table of Contents

  * [Introduction](#introduction)
     * [Motivation and Scope](#motivation-and-scope)
     * [Current Status](#current-status)
     * [Further Reading](#further-reading)
  * [Quickstart](#quickstart)
  * [Available Analyzers](#available-analyzers)
  * [Create an Analyzer](#create-an-analyzer)
  * [Contribute](#contribute)
     * [Community](#community)
  * [Code of Conduct](#code-of-conduct)
  * [License](#license)

source{d} is the company driving the Machine Learning on Code (#MLonCode) movement. Doing Machine Learning on Code consists of applying ML techniques to train models that can cluster, identify and predict useful aspects of source code and software repositories.

source{d} Lookout is the first step towards a full suite of Machine Learning on Code applications for AI-assisted coding, but you can also create your own analyzers without an ML approach.

The benefits of using source{d} Lookout are:

• Keep your code base style/patterns consistent.
• Language agnostic assisted code reviews.
• Identify where to focus your attention on code reviews.
• Automatically warn about common mistakes before human code review.

Currently, source{d} Lookout is in development process.

This repository contains the code of source{d} Lookout and the project documentation, which you can also see properly rendered at https://docs.sourced.tech/lookout.

There are different ways to run Lookout; we recommend to use docker-compose because it's straightforward, but you can learn more about the different ways to run Lookout.

Please refer to the Configuring source{d} Lookout guide for documentation about the config.yml file, and to know how to configure Lookout to analyze your repositories, or to use your own analyzers.

Using Docker Compose you can use the provided docker-compose.yml config file to start Lookout, its dependencies (bblfsh and PostgreSQL) and a dummy analyzer which will add some stats to the watched pull requests.

To do so, clone this repository or download docker-compose.yml.

Create the config.yml file in the same directory where docker-compose.yml is (you can use config.yml.tpl as a template), and then run:

$ docker-compose pull
$ GITHUB_USER=<user> GITHUB_TOKEN=<token> docker-compose up --force-recreate

You can stop it pressing ctrl+c

If you want to try source{d} Lookout with your own analyzer instead of dummy one, you must run it in advance, then set it into config.yml and then run:

$ docker-compose pull
$ GITHUB_USER=<user> GITHUB_TOKEN=<token> docker-compose up --force-recreate lookout bblfsh postgres

If you need to restart the database to a clean state, you must drop the postgres container. To do so, stop running Lookout with ctrl+c and then run:

$ docker rm lookout_postgres_1

This is a list of some of the available analyzers for source{d} Lookout:

If you are developing an Analyzer, or you want more info about how do they work, please check the documentation about Lookout analyzers.

Contributions are more than welcome, if you are interested please take a look at our Contributing Guidelines.

source{d} has an amazing community of developers and contributors who are interested in Code As Data and/or Machine Learning on Code. Please join us! 👋

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All activities under source{d} projects are governed by the source{d} code of conduct.

Affero GPL v3.0 or later, see LICENSE.