  Context: The dire consequences of the COVID-19 pandemic has influenced
development of COVID-19 software i.e., software used for analysis and
mitigation of COVID-19. Bugs in COVID-19 software can be consequential, as
COVID-19 software projects can impact public health policy and user data
privacy. Objective: The goal of this paper is to help practitioners and
researchers improve the quality of COVID-19 software through an empirical study
of open source software projects related to COVID-19. Methodology: We use 129
open source COVID-19 software projects hosted on GitHub to conduct our
empirical study. Next, we apply qualitative analysis on 550 bug reports from
the collected projects to identify bug categories. Findings: We identify 8 bug
categories, which include data bugs i.e., bugs that occur during mining and
storage of COVID-19 data. The identified bug categories appear for 7 categories
of software projects including (i) projects that use statistical modeling to
perform predictions related to COVID-19, and (ii) medical equipment software
that are used to design and implement medical equipment, such as ventilators.
Conclusion: Based on our findings, we advocate for robust statistical model
construction through better synergies between data science practitioners and
public health experts. Existence of security bugs in user tracking software
necessitates development of tools that will detect data privacy violations and
security weaknesses.
