Publication Type

Journal Article

Version

acceptedVersion

Publication Date

6-2016

Abstract

We all want to be associated with long lasting ideas; as originators, or at least, expositors. For a tyro researcher or a seasoned veteran, knowing how long an idea will remain interesting in the community is critical in choosing and pursuing research threads. In the physical sciences, the notion of half-life is often evoked to quantify decaying intensity. In this paper, we study a corpus of 19,000+ papers written by 21,000+ authors across 16 software engineering publication venues from 1975 to 2010, to empirically determine the half-life of software engineering research topics. In the absence of any consistent and well-accepted methodology for associating research topics to a publication, we have used natural language processing techniques to semi-automatically identify and associate a set of topics with a paper. We adapted measures of half-life already existing in the bibliometric context for our study, and also defined a new measure based on publication and citation counts. We find evidence that some of the identified research topics show a mean half-life of close to 15 years, and there are topics with sustaining interest in the community. We report the methodology of our study in this paper, as well as the implications and utility of our results.

Keywords

Big data, software engineering, research, half-life

Discipline

Numerical Analysis and Scientific Computing | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

IEEE Transactions on Big Data

Volume

2

Issue

2

First Page

124

Last Page

137

ISSN

2332-7790

Identifier

10.1109/TBDATA.2016.2580541

Publisher

IEEE

Embargo Period

6-23-2021

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/TBDATA.2016.2580541

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