The future of engineers’ resume

Sourcerer case demo

Sourcerer helps engineers advance career by demonstrating their source code patterns. Sourcerer connects to GitLab, GitHub, Bitbucket and provides statistics and insights, such as the number of code lines written in python, or how often a particular library was used. Sourcerer learns code data and builds a visual profile of the engineer’s abilities, habits, and preferences.

  • Deliverables
    Vue.js development
  • Duration
    June 2018 — November 2018
  • Interested?
    sourcerer.io

Role

We’ve been helping Sourcerer team to polish and optimize their existing Vue.js application. Our work included redeveloping some of the profiles metrics, fixing a number of both critical and minor bugs.

Getting Started

There are two methods to create your account - using GitHub’s and GitLab OAuth or using the Sourcerer app to analyze code locally.

Sourcerer runs and saves analysis locally on your machine, you can determine where you want it to store your results. The analysis data and the statistics are sent to the Sourcerer servers to build your profile. Sourcerer does not upload your code anywhere, and never will.

Sourcerer case summary

Summary shows how much data the profile actually has and how popular it is.

“Wiley is a machine learning engineer, and those guys commit less frequently than, say, web engineers. At any rate, a few hundred commits is the point where I believe that a profile presents someone’s work well. I don’t find other basic statistics such as a number of repos or lines of code as useful: repos are organized dramatically differently in different teams, and lines of code tend to be inflated by boilerplate."

Sergey Surkov, CEO at Sourcerer
Sergey Surkov, CEO at Sourcerer

Overview shows engineers’ work on the timeline — languages used, number of commits and LOCs.

“The first thing to notice here is that the timeline covers a few years of work. We are looking at someone with substantial experience. We can also see that Wiley’s languages and technologies have been very consistent. He writes most of his code in python, and relies on various machine learning and computer vision tools such as numpy, opencv, and scikit.”

Sergey Surkov, CEO at Sourcerer
Sergey Surkov, CEO at Sourcerer
Sourcerer case overview
Sourcerer case technologies

Technologies

Technologies section summarizes technologies such as ‘Deep Learning’ or ‘Web’ by the number of commits that used them and also lists relevant libraries and frameworks.

Fun Facts

“Take a look at commits by day of the week. It helps me understand if the code was authored on weekends or weekdays. If the most code is committed on weekends, it could mean the commits we are looking at came from hobby projects. Weekday commits suggest it’s the main work.”

Sergey Surkov, CEO at Sourcerer
Sergey Surkov, CEO at Sourcerer
Sourcerer case fun facts
Sourcerer case repositories

Repositories

“Many repositories have just one contributor, but I get really excited when I see repos with multiple authors. It is always reassuring to know that engineer had colleagues in a project, and thus likely had to abide by coding standards, work with other people’s code, be careful to not break things, and so on.”

Sergey Surkov, CEO at Sourcerer
Sergey Surkov, CEO at Sourcerer

“The best part is I don’t even need to read all the profiles. I can simply ask Sourcerer to find someone similar to Wiley. Or someone who would be a good fit for my code base. Or someone who would complement the team well. Or all of the above.”

Sergey Surkov, CEO at Sourcerer

Launch & Impact