No abstract

The gender gap is a significant concern facing the software industry as the development becomes more geographically distributed. Widely shared reports indicate that gender differences may be specific to each region. However, how complete can these reports be with little to no research reflective of the Open Source Software (OSS) process and communities software is now commonly developed in? Our study presents a multi-region geographical analysis of gender inclusion on GitHub. This mixed-methods approach includes quantitatively investigating differences in gender inclusion in projects across geographic regions and investigate these trends over time using data from contributions to 21,456 project repositories. We also qualitatively understand the unique experiences of developers contributing to these projects through a survey that is strategically targeted to developers in various regions worldwide. Our findings indicate that gender diversity is low across all parts of the world, with no substantial difference across regions. However, there has been statistically significant improvement in diversity worldwide since 2014, with certain regions such as Africa improving at faster pace. We also find that most motivations and barriers to contributions (e.g., lack of resources to contribute and poor working environment) were shared across regions, however, some insightful differences, such as how to make projects more inclusive, did arise. From these findings, we derive and present implications for tools that can foster inclusion in open source software communities and empower contributions from everyone, everywhere.

No abstract

Software developers have benefited from various sources of knowledge such as forums, question-and-answer sites, and social media platforms to help them in various tasks. Extracting software-related knowledge from different platforms involves many challenges. In this paper, we propose an approach to improve the effectiveness of knowledge extraction tasks by performing crossplatform analysis. Our approach is based on transfer representation learning and word embeddings, leveraging information extracted from a source platform which contains rich domain-related content. The information extracted is then used to solve tasks in another platform (considered as target platform) with less domain-related contents. We first build a word embeddings model as a representation learned from the source platform, and use the model to improve the performance of knowledge extraction tasks in the target platform. We experiment with Software Engineering Stack Exchange and Stack Overflow as source platforms, and two different target platforms, i.e., Twitter and YouTube. Our experiments show that our approach improves performance of existing work for the tasks of identifying software-related tweets and helpful YouTube comments.

No abstract

No abstract

Software developers use a variety of social media channels and tools in order to keep themselves up to date, collaborate with other developers, and find projects to contribute to. Meetup is one of such social media used by software developers to organize community gatherings. Liu et al. characterized Meetup as an event-based social network (EBSN) which contains valuable offline social interactions in addition to online interactions. Recently, Storey et al. found out that Meetup was one of the social channels used by developers. We in this work investigate in detail the dynamics of Meetup groups and events related to software development, which has not been done in any of the previous works.In this work, we performed an empirical study of events and groups present on Meetup which are related to software development. First, we identified 6,317 Meetup groups related to software development and extracted 185,758 events organized by them. Then we took a statistically significant sample of 452 events (95% confidence level with 5% error margin) on which we performed open coding, based on which we were able to develop 9 categories of events (8 main categories +"Others"). Next, we did a popularity analysis of the categories of events and found that Talks by Domain Experts, Hands-on Sessions, and Open Discussions are the most popular categories of events organized by Meetup groups related to software development. Our findings show that more popular categories are those where developers can learn and gain knowledge. On doing a diversity analysis of Meetup groups we found 19.82% of the members on an average are female, which is a larger proportion as compared to numbers reported in previous studies on other social media. We also found that the groups related to technologies such as blockchain and machine learning have become more popular during last 5 years. We also built a topic association graph based on the topics. Our study results show that a lot of software community related information is present in Meetup data,

No abstract