Development/Summer of Code/2015

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Revision as of 14:24, 11 February 2015 by RobertKaye (talk | contribs) (Moderation interface for CritiqueBrainz)


This year Robert Kaye, Ian McEwen and Michael Wiencek will probably be amongst our mentors. That's ruaok (Robert), ianmcorvidae (Ian) and bitmap (Michael) on IRC, if you want to come and speak to us first. Some potential mentors are listed by each project; this is far from a normative list, but it might give you somebody to ask about the project.


This is our set of starting ideas for 2015. Add more ideas if you have them!

Add in site messaging in MusicBrainz

See: MBS-1801

Proper implementation of AreaBot

Proposed mentor: nikki

Re-write area bot to be robust and stable

Finish implementation of SOLR Search

Proposed mentor: ianmcorvidae
Proposed student: mineo

Moderation interface for CritiqueBrainz

Proposed mentor: ruaok

Last year we released beta version of our new CritiqueBrainz project. We have a system for reporting spam, but no good interface to act on reports that come in from it. It would be great to have a friendly UI for moderation tasks like this.

There are a lot of other useful things that can be done with CritiqueBrainz. Some people request support for more MusicBrainz entity types to review. There are a lot of untouched tickets in the bug tracker which can be fixed as a part of this project. Or you can come up with your own idea!


Proposed mentor: ruaok or alastairp

AcousticBrainz is our new project that aims to crowd source acoustic information for all music in the world and to make it available to the public. We already have low-level information about more than a million tracks. What we need is a good way for users and developers to interact with all this data and help improve algorithms that are used to analyze it.

This project should involve implementation of the new module that would allow people create datasets with information that we collect. Datasets need to have multiple classes with associated tracks. These datasets will then be used to train machine learning models.

About proposals

Before you dive in and send a proposal to us through Google, it's a good idea to take some time and learn about the MusicBrainz community. At MusicBrainz we pride ourselves for having a strong community - most of us know each other in some way, and some of us know each other face to face from development summits.

A good way to get a feel of this would be to lurk around in IRC, or to talk about your proposals on the mailing lists.

If you're not sure where to start, Development/Summer of Code/Getting started might help.