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

被引:0
作者
Steinmacher, Igor [1 ]
Clarke, Paul [2 ,3 ]
Tuzun, Eray [4 ]
Britto, Ricardo [5 ,6 ]
机构
[1] No Arizona Univ, Sch Informat Comp & Cyber Syst, Flagstaff, AZ 86011 USA
[2] Dublin City Univ, Sch Comp, Dublin, Ireland
[3] LERO, Limerick, Ireland
[4] Bilkent Univ, Dept Comp Engn, Ankara, Turkiye
[5] Ericsson AB, Karskrona, Sweden
[6] Blekinge Inst Technol, Karskrona, Sweden
关键词
global software engineering; machine learning; software process;
D O I
10.1002/smr.2545
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
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.
引用
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页数:2
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