Tracking the Flow of Ideas through the Programming Languages Literature

M. Greenberg, Kathleen Fisher, David Walker
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引用次数: 3

Abstract

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.
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通过编程语言文献跟踪思想的流动
在过去的20年里,像ICFP、OOPSLA、PLDI和POPL这样的会议是如何发展的?在2007年将OOPSLA的论文征稿活动一般化,或者在2010年将这个伞型会议的名称改为SPLASH,对在那里发表的论文种类有什么影响吗?在主题方面,POPL和PLDI论文如何比较?我是否缺少相关的工作?O'Hearn关于分离逻辑的经典论文中的思想是否改变了POPL中出现的论文类型?提议的项目委员会是否涵盖了会议预期提交的所有内容?如果我们有更好的工具来分析编程语言文献,我们可能能够以数据驱动的方式回答这些问题和其他类似的问题。在本文中,我们探讨了主题建模(机器学习的一个分支)如何帮助编程语言社区更好地理解我们的文献。
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