Team Resilience under Shock: An Empirical Analysis of GitHub Repositories during Early COVID-19 Pandemic
Xuan Lu, Wei Ai, Yixin Wang, Qiaozhu Mei

TL;DR
This study analyzes how GitHub software development teams responded to the early COVID-19 pandemic, revealing that team resilience varies based on pre-pandemic properties and providing insights into factors influencing robustness or fragility.
Contribution
It offers a systematic empirical analysis of remote team resilience during COVID-19, identifying key pre-pandemic factors that predict team robustness or fragility.
Findings
Team productivity and activity levels fluctuated during the pandemic.
Resilience is highly correlated with pre-pandemic team properties.
Certain team characteristics predict robustness or fragility to shocks.
Abstract
While many organizations have shifted to working remotely during the COVID-19 pandemic, how the remote workforce and the remote teams are influenced by and would respond to this and future shocks remain largely unknown. Software developers have relied on remote collaborations long before the pandemic, working in virtual teams (GitHub repositories). The dynamics of these repositories through the pandemic provide a unique opportunity to understand how remote teams react under shock. This work presents a systematic analysis. We measure the overall effect of the early pandemic on public GitHub repositories by comparing their sizes and productivity with the counterfactual outcomes forecasted as if there were no pandemic. We find that the productivity level and the number of active members of these teams vary significantly during different periods of the pandemic. We then conduct a…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsCollaboration in agile enterprises · Software System Performance and Reliability · Big Data and Business Intelligence
