Please use this identifier to cite or link to this item: https://ruomo.lib.uom.gr/handle/7000/1133
Title: An empirical study of COVID-19 related posts on Stack Overflow: Topics and technologies
Authors: Georgiou, Konstantinos
Mittas, Nikolaos
Chatzigeorgiou, Alexander
Angelis, Lefteris
Type: Article
Subjects: FRASCATI::Natural sciences::Computer and information sciences
Keywords: COVID-19
Knowledge-sharing
Pandemic
StackOverflow
Issue Date: Dec-2021
Publisher: Elsevier
Source: The Journal of systems and software
Volume: 182
First Page: 111089
Abstract: 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.
URI: https://doi.org/10.1016/j.jss.2021.111089
https://ruomo.lib.uom.gr/handle/7000/1133
ISSN: 0164-1212
Other Identifiers: 10.1016/j.jss.2021.111089
Appears in Collections:Department of Applied Informatics

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