  This paper explores the structure of research papers in software engineering.
Using text mining, we study 35,391 software engineering (SE) papers from 34
leading SE venues over the last 25 years. These venues were divided, nearly
evenly, between conferences and journals. An important aspect of this analysis
is that it is fully automated and repeatable. To achieve that automation, we
used a stable topic modeling technique called LDADE that fully automates
parameter tuning in LDA. Using LDADE, we mine 11 topics that represent much of
the structure of contemporary SE. The 11 topics presented here should not be
"set in stone" as the only topics worthy of study in SE. Rather our goal is to
report that (a) text mining methods can detect large scale trends within our
community; (b) those topic change with time; so (c) it is important to have
automatic agents that can update our understanding of our community whenever
new data arrives.
