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Bayesian data analysis in empirical software engineering---The case of missing data

Chapter in book
Authors Richard Torkar
Robert Feldt
Carlo A. Furia
Published in Contemporary Empirical Methods in Software Engineering
ISBN 978-3-030-32488-9
Publisher Springer International Publishing
Publication year 2020
Published at Institutionen för data- och informationsteknik, Software Engineering (GU)
Language en
Keywords bayesian data analysis
Subject categories Software Engineering, Statistics, computer and systems science


Bayesian data analysis (BDA) is today used by a multitude of research disciplines. These disciplines use BDA as a way to embrace uncertainty by using multilevel models and making use of all available information at hand. In this chapter, we first introduce the reader to BDA and then provide an example from empirical software engineering, where we also deal with a common issue in our field, i.e., missing data. The example we make use of presents the steps done when conducting state of the art statistical analysis. First, we need to understand the problem we want to solve. Second, we conduct causal analysis. Third, we analyze non-identifiability. Fourth, we conduct missing data analysis. Finally, we do a sensitivity analysis of priors. All this before we design our statistical model. Once we have a model, we present several diagnostics one can use to conduct sanity checks. We hope that through these examples, the reader will see the advantages of using BDA. This way, we hope Bayesian statistics will become more prevalent in our field, thus partly avoiding the reproducibility crisis we have seen in other disciplines.

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