Editorial: Machine learning, software process, and global software engineering

buir.contributor.authorTüzün, Eray
buir.contributor.orcidTüzün, Eray|0000-0002-5550-7816
dc.citation.epagee2545-2en_US
dc.citation.issueNumber6
dc.citation.spagee2545-1
dc.citation.volumeNumber35
dc.contributor.authorSteinmacher, I.
dc.contributor.authorClarke, P.
dc.contributor.authorTüzün, Eray
dc.contributor.authorBritto, R.
dc.date.accessioned2024-03-22T13:39:19Z
dc.date.available2024-03-22T13:39:19Z
dc.date.issued2023-01-30
dc.departmentDepartment of Computer Engineering
dc.description.abstractOn 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.
dc.description.provenanceMade available in DSpace on 2024-03-22T13:39:19Z (GMT). No. of bitstreams: 1 Editorial_machine_learning,_software_process,_and_globalsoftware_engineering.pdf: 155226 bytes, checksum: 30ef21815ab32463c03597f181babd3d (MD5) Previous issue date: 2023-01-30en
dc.identifier.doi10.1002/smr.2545en_US
dc.identifier.eissn2047-7481en_US
dc.identifier.urihttps://hdl.handle.net/11693/115095en_US
dc.language.isoEnglishen_US
dc.publisherJohn Wiley and Sons Ltden_US
dc.relation.isversionofhttps://dx.doi.org/10.1002/smr.2545
dc.rightsCC BY-NC-ND 4.0 DEED [Attribution-NonCommercial-NoDerivs 4.0 International)
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.source.titleJournal of Software: Evolution and Process
dc.subjectGlobal software engineering
dc.subjectMachine learning
dc.subjectSoftware process
dc.titleEditorial: Machine learning, software process, and global software engineering
dc.typeEditorial

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