Información de la conferencia
MSR 2021: Working Conference on Mining Software Repositories
https://conf.researchr.org/home/msr-2021
Día de Entrega:
2021-01-05
Fecha de Notificación:
2021-02-22
Fecha de Conferencia:
2021-05-23
Ubicación:
Madrid, Spain
Años:
18
CCF: c   CORE: a   QUALIS: b1   Vistas: 21274   Seguidores: 39   Asistentes: 3

Solicitud de Artículos
The technical track of MSR 2021 solicits high-quality submissions on a wide range of topics related to artificial intelligence (AI), machine learning (ML), and data science (DS) in one or more of the following three main themes.

1. AI/ML/DS and SE

The analysis should aim to improve understanding of development processes and practices or aid in the development of new techniques or models to support software developers. This includes (but is not limited to) analysis or models for:

    commits,
    execution traces and logs,
    interaction data,
    code review data,
    natural language artifacts,
    software licenses and copyrights,
    app store data,
    programming language features,
    release information,
    CI logs,
    deployment and delivery,
    test data,
    runtime information,
    software ecosystems,
    defect and software quality data,
    human and social aspects of development,
    development process,
    energy profile data.

2. New techniques, tools, and models.

The techniques, tools, and models should facilitate new ways to mine, analyze, or model software data. A submission could include (but is not limited to) techniques, tools, or models to:

    capture new forms of data,
    integrate data from multiple sources,
    visualize software data,
    model software data,
    solve SE problems,
    improve AI/ML/DS.

3. Considerations related to AI/ML/DS and SE.

These submissions should reflect on the current state-of-the-art research methods or current practices in mining, analyzing, or modeling software data. These submissions can also propose new research methods or guidelines. This theme includes topics such as (but not limited to)

    privacy of collected data,
    ethics of mining, analyzing, or modelling software data,
    biases in software data, analyses, and tools,
    fairness in software data, analyses, and tools,
    Replication studies.
Última Actualización Por Dou Sun en 2020-10-18
Coeficiente de Aceptación
AñoEnviadosAceptadosAceptados(%)
2012862933.7%
2011782633.3%
2010672131.3%
2009652640%
2008422252.4%
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b1EuroGPEuropean Conference on Genetic Programming2015-11-152016-01-042016-03-30
Revistas Relacionadas
CCFNombre CompletoFactor de ImpactoEditorISSN
Annals of Software Engineering Springer1022-7091
Environmental Modelling & Software5.288Elsevier1364-8152
Programming and Computer Software0.105Springer0361-7688
bEmpirical Software Engineering2.522Springer1382-3256
International Journal of Agent-Oriented Software Engineering Inder Science Publishers1746-1375
cSoftware Quality Journal1.642Springer0963-9314
Mathematical Methods of Operations Research1.343Springer1432-2994
Advances in Engineering Software4.141Elsevier0965-9978
aIEEE Transactions on Software Engineering4.778IEEE0098-5589
ACM SIGSOFT Software Engineering Notes ACM0163-5948
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