Google Summer of Code Gender Diversity: An analysis of the last 4 editions

Abstract

This work presents a comprehensive research about the participationof men and women in the area of Information and CommunicationsTechnology (ICT) through data extracted from the last foureditions of Google Summer of Code (GSoC). The goal of this workis to find Association Rules between gender characteristics andcoding using the Apriori Algorithm. A total of 61 association ruleswere generated through the aforementioned algorithm, being 22 ofthem found only in the data set with the women, 24 found only withthe men, and 15 applicable to both sets. We can cite as one of themain findings of this work the fact that the representativeness ofwomen in GSoC is decreasing in the last few years. Despite this, therepresentativeness of women in GSoC is above average, accordingto what has been reported in other studies in the literature in whichwomen are underrepresented. When it comes to the most utilizedtechnologies, we have “Python", “Java", “C++", “C" and “JavaScript"in the top. Analyzing technologies, it’s possible to realize that themain utilized technologies for men and women are similar, but, ingeneral, men are more likely linked to programming languages.The most common project topics are: “Event Management", “Web",“Web Development", “Data Science" and “Cloud" in the top. Thiscan represent how diverse the project topics of the database are,but not necessarily has something related to gender.