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Editorial: Machine learning, software process, and global software engineering
Northern Arizona University, USA.
Dublin City University, Ireland.
LERO, Ireland.
Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.ORCID iD: 0000-0002-7220-9570
2023 (English)In: Journal of Software: Evolution and Process, ISSN 2047-7473, E-ISSN 2047-7481, Vol. 35, no 6, article id e2545Article in journal, Editorial material (Other academic) Published
Abstract [en]

On June 26–28, 2020, the International Conference on Software and Systems Processes (ICSSP 2020) and the International Conference on Global Software Engineering (ICGSE 2020) were held in virtual settings during the first year of the COVID pandemic. Several submissions to the joint event have been selected for inclusion in this special issue, focusing on impactful and timely contributions to machine learning (ML). At present, many in our field are enthusiastic about the potential of ML, yet some risks should not be casually overlooked or summarily dismissed. Each ML implementation is subtly different from any other implementation, and the risk profile varies greatly based on the approach adopted and the implementation context. The ICSSP/ICGSE 2020 Program Committees have encouraged submissions that explore the risks and benefits associated with ML so that the important discussion regarding ML efficacy and advocacy can be further elaborated. Four contributions have been included in this special issue. © 2023 John Wiley & Sons, Ltd.

Place, publisher, year, edition, pages
John Wiley & Sons, 2023. Vol. 35, no 6, article id e2545
Keywords [en]
global software engineering, machine learning, software process
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:bth-24376DOI: 10.1002/smr.2545Scopus ID: 2-s2.0-85149326527OAI: oai:DiVA.org:bth-24376DiVA, id: diva2:1744136
Available from: 2023-03-17 Created: 2023-03-17 Last updated: 2023-06-19Bibliographically approved

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Britto, Ricardo

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