short-paper

The emotional side of software developers in JIRA

Authors:

Alessandro Murgia,

Giuseppe Destefanis,

Parastou Tourani,

Roberto Tonelli,

Michele Marchesi,

Bram AdamsAuthors Info & Claims

MSR '16: Proceedings of the 13th International Conference on Mining Software Repositories

Pages 480 - 483

https://doi.org/10.1145/2901739.2903505

Published: 14 May 2016 Publication History

Abstract

Issue tracking systems store valuable data for testing hypotheses concerning maintenance, building statistical prediction models and (recently) investigating developer affectiveness. For the latter, issue tracking systems can be mined to explore developers emotions, sentiments and politeness---affects for short. However, research on affect detection in software artefacts is still in its early stage due to the lack of manually validated data and tools.

In this paper, we contribute to the research of affects on software artefacts by providing a labeling of emotions present on issue comments.

We manually labeled 2,000 issue comments and 4,000 sentences written by developers with emotions such as love, joy, surprise, anger, sadness and fear. Labeled comments and sentences are linked to software artefacts reported in our previously published dataset (containing more than 1K projects, more than 700K issue reports and more than 2 million issue comments). The enriched dataset presented in this paper allows the investigation of the role of affects in software development.

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cover image ACM Conferences

MSR '16: Proceedings of the 13th International Conference on Mining Software Repositories

May 2016

544 pages

ISBN:9781450341868

DOI:10.1145/2901739

General Chair:
Miryung Kim
University of California, Los Angeles
,
Program Chairs:
Romain Robbes
University of Chile, Chile
,
Christian Bird
Microsoft Research

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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ACM: Association for Computing Machinery
SIGSOFT: ACM Special Interest Group on Software Engineering
IEEE-CS\DATC: IEEE Computer Society
TCSE: IEEE Computer Society's Tech. Council on Software Engin.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 May 2016

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ICSE '16

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ICSE '16: 38th International Conference on Software Engineering

May 14 - 22, 2016

Texas, Austin

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Ploscă TCuriac CCuriac D(2024)Investigating Semantic Differences in User-Generated Content by Cross-Domain Sentiment Analysis MeansApplied Sciences10.3390/app1406242114:6(2421)Online publication date: 13-Mar-2024
https://doi.org/10.3390/app14062421
Anwar ZAfzal HAltaf NKadry SKim J(2024)Fuzzy ensemble of fined tuned BERT models for domain-specific sentiment analysis of software engineering datasetPLOS ONE10.1371/journal.pone.030027919:5(e0300279)Online publication date: 28-May-2024
https://doi.org/10.1371/journal.pone.0300279
Zhang TIrsan IThung FLo D(2024)Revisiting Sentiment Analysis for Software Engineering in the Era of Large Language ModelsACM Transactions on Software Engineering and Methodology10.1145/369700934:3(1-30)Online publication date: 24-Sep-2024
https://dl.acm.org/doi/10.1145/3697009
Coutinho DCito LLima MArantes BAlves Pereira JArriel JGodinho JMartins VLibório PLeite LGarcia AAssunção WSteinmacher IBaffa AFonseca B(2024)"Looks Good To Me ;-)": Assessing Sentiment Analysis Tools for Pull Request DiscussionsProceedings of the 28th International Conference on Evaluation and Assessment in Software Engineering10.1145/3661167.3661189(211-221)Online publication date: 18-Jun-2024
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Kumar JChimalakonda SBaysal OLinares-Vasquez MMoran KSteinmacher I(2024)What Do Developers Feel About Fast-Growing Programming Languages? An Exploratory StudyProceedings of the 32nd IEEE/ACM International Conference on Program Comprehension10.1145/3643916.3644422(178-189)Online publication date: 15-Apr-2024
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Guru SKumar L(2024)Investigation of Predictive Power of Sentiment Analysis Model Developed Using Different Word Embedding TechniquesData-Driven Decision Making10.1007/978-981-97-2902-9_2(27-58)Online publication date: 28-Jul-2024
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