Finding Trends in Software Research
It provides an automated, repeatable method for tracking research trends in software engineering, which is incremental as it applies existing text mining techniques to a new dataset.
This paper tackled the problem of analyzing trends in software engineering research by using text mining on 35,391 papers over 25 years, resulting in the identification of 11 topics that reveal large-scale, time-varying patterns in the field.
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.