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Harmonising Contributions: Exploring Diversity in Software Engineering through CQA Mining on Stack Overflow.
- Source :
- ACM Transactions on Software Engineering & Methodology; Sep2024, Vol. 33 Issue 7, p1-54, 54p
- Publication Year :
- 2024
-
Abstract
- The need for collective intelligence in technology means that online Q&A platforms, such as Stack Overflow and Reddit, have become invaluable in building the global knowledge ecosystem. Despite literature demonstrating a prevalence of inclusion and contribution disparities in online communities, studies investigating the underlying reasons behind such fluctuations remain scarce. The current study examines Stack Overflow users' contribution profiles, both in isolation and relative to various diversity metrics, including GDP and access to electricity. This study also examines whether such profiles propagate to the city and state levels, supplemented by granular data such as per capita income and education, before validating quantitative findings using content analysis. We selected 143 countries and compared the profiles of their respective users to assess implicit diversity-related complications that impact how users contribute. Results show that countries with high GDP, prominent R&D presence, less wealth inequality and sufficient access to infrastructure tend to have more users, regardless of their development status. Similarly, cities and states where technology is more prevalent (e.g., San Francisco and New York) have more users who tend to contribute more often. Qualitative analysis reveals distinct communication styles based on users' locations. Urban users exhibited assertive, solution-oriented behaviour, actively sharing information. Conversely, rural users engaged through inquiries and discussions, incorporating personal anecdotes, gratitude and conciliatory language. Findings from this study may benefit scholars and practitioners, allowing them to develop sustainable mechanisms to bridge the inclusion and diversity gaps. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 1049331X
- Volume :
- 33
- Issue :
- 7
- Database :
- Complementary Index
- Journal :
- ACM Transactions on Software Engineering & Methodology
- Publication Type :
- Academic Journal
- Accession number :
- 179975185
- Full Text :
- https://doi.org/10.1145/3672453