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

An Exploratory Characterization of Bugs in COVID-19 Software Projects