Citation and Topic Analysis of the ESEM Papers

2015 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM) Pub Date : 2015-11-09 DOI:10.1109/ESEM.2015.7321193

Päivi Raulamo-Jurvanen, M. Mäntylä, V. Garousi

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引用次数: 10

Abstract

Context: The pool of papers published in ESEM. Objective: To utilize citation analysis and automated topic analysis to characterize the SE research literature over the years focusing on those papers published in ESEM. Method: We collected data from Scopus database consisting of 513 ESEM papers. For thematic analysis, we used topic modeling to automatically generate the most probable topic distributions given the data. Results: Nearly 42% of the papers have not been cited at all but the effect seems to wear off as time passes. Using text mining of article titles and abstracts, we found that currently the most popular research topics in the ESEM community are: systematic reviews, testing, defects, cost estimation, and team work. Conclusions: While this study analyzes the paper pool of the ESEM symposium, the approach can easily be applied to any other sub-set of SE papers to conduct large scale studies. Due to large volumes of research in SE, we suggest using the automated analysis of bibliometrics as we have done in this paper.

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ESEM论文的引文和主题分析

背景:在ESEM上发表的论文池。目的:利用引文分析和自动主题分析对近年来发表在ESEM上的SE研究文献进行特征分析。方法:从Scopus数据库中检索ESEM论文513篇。对于主题分析，我们使用主题建模来自动生成给定数据的最可能主题分布。结果:近42%的论文根本没有被引用，但随着时间的推移，这种影响似乎逐渐消失。通过对文章标题和摘要的文本挖掘，我们发现当前ESEM社区中最流行的研究主题是:系统评审、测试、缺陷、成本估算和团队合作。结论:虽然本研究分析了ESEM研讨会的论文池，但该方法可以很容易地应用于任何其他SE论文子集进行大规模研究。由于SE的大量研究，我们建议使用文献计量学的自动分析，正如我们在本文中所做的那样。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

2015 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)

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