The COVID-19 outbreak, also known as the coronavirus pandemic, has left its mark on every aspect of our lives and at the time of this writing is still an ongoing battle. Beyond the immediate global-wide health response, the pandemic has triggered a significant number of IT initiatives to track, visualize, analyze and potentially mitigate the phenomenon. For individuals or organizations interested in developing COVID-19 related software, knowledge-sharing communities such as Stack Overflow proved to be an effective source of information for tackling commonly encountered problems. As an additional contribution to the investigation of this unprecedented health crisis and to assess how fast and how well the community of developers has responded, we performed a study on COVID-19 related posts in Stack Overflow. In particular, we profiled relevant questions based on key post features and their evolution, identified the most prominent technologies adopted for developing COVID-19 software and their interrelations and focused on the most persevering problems faced by developers. For the analysis of posts we employed descriptive statistics, Association Rule Graphs, Survival Analysis and Latent Dirichlet Allocation. The results reveal that the response of the developers’ community to the pandemic was immediate and that the interest of developers on COVID-19 related challenges was sustained after its initial peak. In terms of the problems addressed, the results show a clear focus on COVID-19 data collection, analysis and visualization from/to the web, in line with the general needs for monitoring the pandemic.

COVID-19 爆发，也称为冠状病毒大流行，已经在我们生活的方方面面留下了印记，在撰写本文时，这场战斗仍在继续。除了即时的全球范围内的卫生响应之外，大流行还引发了大量 IT 计划来跟踪、可视化、分析并可能减轻这种现象。对于有兴趣开发 COVID-19 相关软件的个人或组织，事实证明，Stack Overflow 等知识共享社区是解决常见问题的有效信息来源。为了进一步调查这场前所未有的健康危机，并评估开发者社区的反应速度和效果，我们对 Stack Overflow 中与 COVID-19 相关的帖子进行了研究。特别是，我们根据帖子的关键特征及其演变分析了相关问题，确定了开发 COVID-19 软件所采用的最突出的技术及其相互关系，并重点关注了开发人员面临的最顽固的问题。对于帖子的分析，我们采用了描述性统计、关联规则图、生存分析和 Latent Dirichlet 分配。结果表明，开发者社区对大流行病的反应是立竿见影的，并且开发者对 COVID-19 相关挑战的兴趣在最初的高峰期后得以持续。就所解决的问题而言，结果表明明确关注从/到网络的 COVID-19 数据收集、分析和可视化，符合监测大流行的一般需求。确定了开发 COVID-19 软件所采用的最突出的技术及其相互关系，并专注于开发人员面临的最顽固的问题。对于帖子的分析，我们采用了描述性统计、关联规则图、生存分析和 Latent Dirichlet 分配。结果表明，开发者社区对大流行病的反应是立竿见影的，并且开发者对 COVID-19 相关挑战的兴趣在最初的高峰期后依然存在。就解决的问题而言，结果表明明确关注从/到网络的 COVID-19 数据收集、分析和可视化，符合监测大流行的一般需求。确定了开发 COVID-19 软件所采用的最突出的技术及其相互关系，并专注于开发人员面临的最顽固的问题。对于帖子的分析，我们采用了描述性统计、关联规则图、生存分析和 Latent Dirichlet 分配。结果表明，开发者社区对大流行病的反应是立竿见影的，并且开发者对 COVID-19 相关挑战的兴趣在最初的高峰期后依然存在。就解决的问题而言，结果表明明确关注从/到网络的 COVID-19 数据收集、分析和可视化，符合监测大流行的一般需求。