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

This work presents a comprehensive research about the participation of men and women in the area of Information and Communications Technology (ICT) through data extracted from the last four editions of Google Summer of Code (GSoC). The goal of this work is to find Association Rules between gender characteristics and coding using the Apriori Algorithm. A total of 61 association rules were generated through the aforementioned algorithm, being 22 of them found only in the data set with the women, 24 found only with the men, and 15 applicable to both sets. We can cite as one of the main findings of this work the fact that the representativeness of women in GSoC is decreasing in the last few years. Despite this, the representativeness of women in GSoC is above average, according to what has been reported in other studies in the literature in which women are underrepresented. When it comes to the most utilized technologies, we have “Python", “Java", “C++", “C" and “JavaScript" in the top. Analyzing technologies, it’s possible to realize that the main utilized technologies for men and women are similar, but, in general, 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. This can represent how diverse the project topics of the database are, but not necessarily has something related to gender.