Github Dating Simulator college pupil, finding love could be difficult. Similarly, findi

Github Dating Simulator college pupil, finding love could be difficult. Similarly, findi

As a senior school pupil, finding love may be difficult. Likewise, finding individuals prepared to invest their week-end teaming up beside me at a hackathon is hard as well.

At hackCooper 2016, we caused Isabella Berry to resolve both of these difficulties with Github Dating Simulator, a software that analyzes compatibility between Github users by utilizing graph concept and also the energy of love. It is perhaps perhaps not just a dating simulator into the old-fashioned sense—rather, it is a internet application that enables individuals in search of hackathon groups to get individuals with comparable coding backgrounds to prevent the trouble of scrambling to get a group in the last second.


Github Dating Simulator will come in two tastes. “Dating mode” permits a user to input two Github usernames to find out just just just how suitable these are generally. “Team generation mode” (the greater amount of practical mode) enables a person to input a list of Github usernames, will get back the perfect pairings for every regarding the users. It enables them to create options that are several such as for instance exactly how many individuals should really be contained in each group.

For each match that Github Dating Simulator analyzes, it outputs a “compatibility” percentage, which will be basically the program’s confidence level why these two different people should be able to come together well.

Only for fun, in addition creates a summary of “first date ideas”, that are essentially arbitrarily created task some ideas in line with the typical languages shared between each individual to simply help kickstart the ideation procedure. (so when it discovers really suitable matches, in addition it outputs a listing of “first date areas”—a.k.a. upcoming hackathons.)


I happened to be in charge of the UI design and also the implementation that is technical this task. Probably one of the most projects that are statistically intensive labored on to date, Github Dating Simulator hinges on a mix of the Github API and graph algorithms to effectively and accurately pair users.

Pairing Algorithm

To generate matchings, it appears in the language use of each individual and compares it on a level that is experience-based those of this other users. Which means that an individual who possesses great deal of repositories printed in Ruby are going to be marked as an “expert” while an individual who has just only written 70 lines of Ruby are going to be marked as being a “beginner”. This permits users become matched along with other programmers proportional with their level of skill, that allows programmers to work alongside folks of comparable coding backgrounds, making for a easier hackathon experience overall.

(this might be a thing that had been highly contested, as you might choose to fit people with additional experiences with particular development languages with individuals who have less experience for an even more experience that is educational. Maybe a choice for this kind of matching algorithm will be the next upgrade.)

My records and sketches when it comes to UI design.

On a graph, each user is plotted off their users with various paths of varying “lengths”. Each individual is really a node in the graph, and every path represents a language that is common two users. (If two users try not to share any languages that are common they’ll not have paths among them.) Path length is determined by the mean square distinction of each of the languages a person understands.

The algorithm attempts to discover the path that is shortest (essentially, comparable experiences with particular languages) between two users. After that it aggregates every one of the paths between two users in to a single “compatibility” metric predicated on a logarithmic scale, after which starts producing matches beginning with the compatibility percentage that is highest. When a person happens to be matched with another individual, it’ll delete both users through the graph so they really cannot be matched once more. The algorithm continues until all users have already been matched or there aren’t any more available users to match.


One of many challenges that are major we went into had been that the Github API has price restricting, which prevents one from making a lot of API needs in an offered period of time. To resolve this issue, we applied a pseudo-caching system with a PostgreSQL database. With the Github API’s conditional demand function, we just make the full demand to Github that the data at each location has been changed if they tell us. Otherwise, we just count on formerly saved information that it hasn’t changed since we know.

Presenting Github Dating Simulator at the judging expo.

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