76 research outputs found
Signaling Diversity and Inclusion for Open Source Project Health
Open source projects often consist of mostly white, male, and English-speaking software developers. For the past decade, women and people from minority backgrounds have sought to bring more diversity to open source projects and make them more inclusive. This presentation summarizes research findings of how projects signal diversity and inclusion to attract these people. A key finding is that signals for diversity and inclusion are wanted, but projects struggle to put them into practice. The presentation discusses implications for signaling theory and open source projects
Gendered behavior as a disadvantage in open source software development
Women are severely marginalized in software development, especially in open
source. In this article we argue that disadvantage is more due to gendered
behavior than to categorical discrimination: women are at a disadvantage
because of what they do, rather than because of who they are. Using data on
entire careers of users from GitHub.com, we develop a measure to capture the
gendered pattern of behavior: We use a random forest prediction of being female
(as opposed to being male) by behavioral choices in the level of activity,
specialization in programming languages, and choice of partners. We test
differences in success and survival along both categorical gender and the
gendered pattern of behavior. We find that 84.5% of women's disadvantage
(compared to men) in success and 34.8% of their disadvantage in survival are
due to the female pattern of their behavior. Men are also disadvantaged along
their interquartile range of the female pattern of their behavior, and users
who don't reveal their gender suffer an even more drastic disadvantage in
survival probability. Moreover, we do not see evidence for any reduction of
these inequalities in time. Our findings are robust to noise in gender
recognition, and to taking into account particular programming languages, or
decision tree classes of gendered behavior. Our results suggest that fighting
categorical gender discrimination will have a limited impact on gender
inequalities in open source software development, and that gender hiding is not
a viable strategy for women
How Successful Are Open Source Contributions From Countries with Different Levels of Human Development?
Are Brazilian developers less likely to have a contribution accepted than
their peers from, say, the United Kingdom? In this paper we studied whether the
developers' location relates to the outcome of a pull request. We curated the
locations of 14k contributors who performed 44k pull requests to 20 open source
projects. Our results indeed suggest that developers from countries with low
human development indexes (HDI) not only perform a small fraction of the
overall pull requests, but they also are the ones that face rejection the most.Comment: 5 pages, 1 figur
A Cross-Repository Model for Predicting Popularity in GitHub
Social coding platforms, such as GitHub, can serve as natural laboratories
for studying the diffusion of innovation through tracking the pattern of code
adoption by programmers. This paper focuses on the problem of predicting the
popularity of software repositories over time; our aim is to forecast the time
series of popularity-related events (code forks and watches). In particular, we
are interested in cross-repository patterns-how do events on one repository
affect other repositories? Our proposed LSTM (Long Short-Term Memory) recurrent
neural network integrates events across multiple active repositories,
outperforming a standard ARIMA (Auto-Regressive Integrated Moving Average) time
series prediction based on the single repository. The ability of the LSTM to
leverage cross-repository information gives it a significant edge over standard
time series forecasting.Comment: 6 page
Gender Differences in Equity Crowdfunding
Online peer-to-peer investment platforms are increasingly popular venues for entrepreneurs and investors to engage in financial transactions without the involvement of banks and loan managers. Despite their purported transparency and lack of bias, it is unclear whether social inequalities present in traditional capital markets transfer to these platforms as well, impeding their hoped revolutionary potential. In this paper we analyze nearly four years' worth of data from one of the leading UK-based equity crowdfunding platforms. Specifically, we investigate gender-related differences in patterns of entrepreneurship, investment, and success. In agreement with offline trends, men have more activity on the platform. Yet, women entrepreneurs benefit of higher success rates in fund-raising, a finding that mimics trends seen on some rewards-based crowdfunding platforms. Surprisingly, we also find that female investors tend to choose campaigns that have lower success rates. Our findings contribute to a better understanding of gender-related discrepancies in success on the online capital market and point to differences in activity that are key factors in the apparent patterns of gender inequality
Making Quantum Computing Open: Lessons from Open-Source Projects
Quantum computing (QC) is an emerging computing paradigm with potential to
revolutionize the field of computing. QC is a field that is quickly developing
globally and has high barriers of entry. In this paper we explore both
successful contributors to the field as well as wider QC community with the
goal of understanding the backgrounds and training that helped them succeed. We
gather data on 148 contributors to open-source quantum computing projects
hosted on GitHub and survey 46 members of QC community. Our findings show that
QC practitioners and enthusiasts have diverse backgrounds, with most of them
having a PhD and trained in physics or computer science. We observe a lack of
educational resources on quantum computing. Our goal for these findings is to
start a conversation about how best to prepare the next generation of QC
researchers and practitioners
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