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arXiv 1608.08100cs.SE
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发现软件研究的趋势

Finding Trends in Software Research

George Mathew, Amritanshu Agrawal, Tim Menzies

摘要:

本文探讨了软件工程研究论文的结构。使用文本挖掘,我们研究了35,391软件工程(SE)论文从34个领先的SE场地在过去的25年。这些场地几乎平均分配给会议和期刊。这种分析的一个重要方面是它是完全自动化和可重复的。为了实现这种自动化,我们使用了一种名为LDADE的稳定主题建模技术,它可以完全自动化LDA中的参数调优。使用LDADE,我们挖掘11个主题,代表了当代SE的结构。这里提出的11个主题不应该是“一成不变”的,因为只有这些主题值得在SE中学习。相反,我们的目标是报告(a)文本挖掘方法可以检测我们社区内的大规模趋势;(b)这些主题随着时间的推移而变化;因此(c)重要的是要有自动代理,只要新数据到达,就可以更新我们对社区的理解。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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