source{d} Lookout

Service for assisted code review, that allows running custom code Analyzers on pull requests.

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Introduction

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
  • Motivation and Scope
  • Current Status
  • Further Reading
• Quickstart
• Available Analyzers
• Create an Analyzer
• Contribute
  • Community
• Code of Conduct
• License

Motivation and Scope

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.

Current Status

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

Further Reading

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.

Quickstart

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

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

There is docker-compose.yml config file for Docker Compose to start source{d} 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 directly.

Create the config.yml file in the same directory where docker-compose.yml is. You can use config.yml.tpl as a template. Then run:

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

You can stop it by 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 reset the database to a clean state, you should drop the postgres container. To do so, stop running source{d} Lookout with ctrl+c and then execute:

$ docker rm lookout_postgres_1

Available Analyzers

This is the list of the known implemented analyzers for source{d} Lookout:

Create an Analyzer

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

Contribute

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

Community

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! 👋

• Slack
• Twitter
• Email

Code of Conduct

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

License

Affero GPL v3.0 or later, see LICENSE.