  Contemporary social coding platforms such as GitHub facilitate collaborative
distributed software development. Developers engaged in these platforms often
use machine accounts (bots) for automating effort-intensive or repetitive
activities. Determining whether a contributor corresponds to a bot or a human
account is important in socio-technical studies, for example, to assess the
positive and negative impact of using bots, analyse the evolution of bots and
their usage, identify top human contributors, and so on. BoDeGHa is one of the
bot detection tools that have been proposed in the literature. It relies on
comment activity within a single repository to predict whether an account is
driven by a bot or by a human. This paper presents preliminary results on how
the effectiveness of BoDeGHa can be improved by combining the predictions
obtained from many repositories at once. We found that doing this not only
increases the number of cases for which a prediction can be made but that many
diverging predictions can be fixed this way. These promising, albeit
preliminary, results suggest that the "wisdom of the crowd" principle can
improve the effectiveness of bot detection tools.
