Abstract
Empirical Software Engineering (ESE) roots back to the 1970s and has since then gained growing recognition as the standard approach to scientific inquiry in the context of software engineering. Many different quantitative and qualitative research methods have been described and supplied with guidelines and checklists and several books have been written about good practice in ESE. With the emerging amount of data being produced during software development, a new paradigm of scientific inquiry has gained much attention, i.e., Data Science (DS). The goal of this chapter is to discuss whether DS could replace traditional ESE or, if it does not replace it, how traditional ESE could benefit from adopting DS practices—and vice versa. In this chapter, we first give some general background information about ESE and DS, then we describe in more detail how both paradigms are typically used in the context of software engineering research and what are their respective strengths and weaknesses. Finally, we illustrate with the help of an industry-driven case example how both paradigms, ESE and DS, could benefit from each other if used in combination.
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This research was partly supported by the institutional research grant IUT20-55 of the Estonian Research Council and the Estonian Centre of Excellence in ICT Research (EXCITE).
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Scott, E., Milani, F., Pfahl, D. (2020). Data Science and Empirical Software Engineering. In: Felderer, M., Travassos, G. (eds) Contemporary Empirical Methods in Software Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-32489-6_8
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