Extra Seminar Computer Science
|When:||Mo 14-01-2019 09:00 - 10:00|
The Relevance of Application Domains in Empirical Findings
Research on empirical software engineering has increasingly used
data from online repositories or collective efforts. The latest trends
for researchers is to gather as much data as possible to (i) prevent
bias in the representation of a small sample, (ii) work with a sample
as close as the population itself, and (iii) showcase the performance
of existing or new tools in treating vast amount of data.
The effects of harvesting enormous amounts of data have been
only marginally considered so far: data could be corrupted; reposi-
tories could be forked; and developer identities could be duplicated.
In this paper we posit that there is a fundamental flaw in harvesting
large amounts of data, and when generalising the conclusions: the
application domain, or context, of the analysed systems must be
the primary factor for the cluster sampling of FOSS projects.
In this talk we analyse a sample of software systems, and using
an existing approach based on Latent Dirichlet Allocation (LDA), we
derive their application domains. We extract a suite structural OO
metrics from each project, and cluster projects by domains: we show
that most of the chosen metrics come from different populations,
and are based on the application domains.