文章基本信息

标题：Tracking the Flow of Ideas through the Programming Languages Literature
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作者：Michael Greenberg ; Kathleen Fisher ; David Walker 等
期刊名称：LIPIcs : Leibniz International Proceedings in Informatics
电子版ISSN：1868-8969
出版年度：2015
卷号：32
页码：140-155
DOI：10.4230/LIPIcs.SNAPL.2015.140
出版社：Schloss Dagstuhl -- Leibniz-Zentrum fuer Informatik
摘要：How have conferences like ICFP, OOPSLA, PLDI, and POPL evolved over the last 20 years? Did generalizing the Call for Papers for OOPSLA in 2007 or changing the name of the umbrella conference to SPLASH in 2010 have any effect on the kinds of papers published there? How do POPL and PLDI papers compare, topic-wise? Is there related work that I am missing? Have the ideas in O'Hearn's classic paper on separation logic shifted the kinds of papers that appear in POPL? Does a proposed program committee cover the range of submissions expected for the conference? If we had better tools for analyzing the programming language literature, we might be able to answer these questions and others like them in a data-driven way. In this paper, we explore how topic modeling, a branch of machine learning, might help the programming language community better understand our literature.
关键词：programming languages literature; topic models; irony