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Last updated on Thursday, November 16, 2023
@article{Murgia2018EMSE,
author = {Alessandro Murgia and Marco Ortu and Parastou Tourani and
Bram Adams and Serge Demeyer},
journal = {Empirical Software Engineering},
month = feb,
note = {SCI impact factor 1.393},
number = {1},
publisher = {Springer Science+Business Media},
title = {An exploratory qualitative and quantitative analysis
of emotions in issue report comments of open source
systems},
volume = {23},
year = {2018},
abstract = {Software development ---just like any other human
collaboration--- inevitably evokes emotions like joy
or sadness, which are known to affect the group
dynamics within a team. Today, little is known about
those individual emotions and whether they can be
discerned at all in the development artifacts
produced during a project. This paper analyzes (a)
whether issue reports ---a common development
artifact, rich in content--- convey emotional
information and (b) whether humans agree on the
presence of these emotions. From the analysis of the
issue comments of 117 projects of the Apache Software
Foundation, we find that developers express emotions
(in particular gratitude, joy and sadness). However,
the more context is provided about an issue report,
the more human raters start to doubt and nuance their
interpretation. Based on these results, we
demonstrate the feasibility of a machine learning
classifier for identifying issue comments containing
gratitude, joy and sadness. Such a classifier, using
emotion-driving words and technical terms, obtains a
good precision and recall for identifying the emotion
love, while for joy and sadness a lower recall is
obtained.},
annote = {internationaljournal},
doi = {10.1007/s10664-017-9526-0},
}