Finding Trends in Software Research

Abstract

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.

12 pages, 4 tables, 11 figures, Accepted in IEEE Transactions on Software Engineering 2018

References in corpus (4)

• On Smoothing and Inference for Topic Models
• What is Wrong with Topic Modeling? (and How to Fix it Using Search-based Software Engineering)
• Tuning for Software Analytics: is it Really Necessary?
• PageRank for ranking authors in co-citation networks

Cited by in corpus (7)

• Software Artifact Mining in Software Engineering Conferences: A Meta-Analysis
• Open Reproducible Publication Research
• Crowdsourcing the State of the Art(ifacts)
• Citations in Software Engineering -- Paper-related, Journal-related, and Author-related Factors
• A Bibliometric Study of Asia Pacific Software Engineering Conference from 2010 to 2015
• Can We Benchmark Code Review Studies? A Systematic Mapping Study of Methodology, Dataset, and Metric
• The General Index of Software Engineering Papers