Publication:
A CMMI-based automated risk assessment framework

dc.contributor.authorMorakot Choetkiertikulen_US
dc.contributor.authorHoa Khanh Damen_US
dc.contributor.authorAditya Ghoseen_US
dc.contributor.authorThanwadee T. Sunetnantaen_US
dc.contributor.otherUniversity of Wollongongen_US
dc.contributor.otherMahidol Universityen_US
dc.date.accessioned2018-11-09T02:10:42Z
dc.date.available2018-11-09T02:10:42Z
dc.date.issued2014-01-01en_US
dc.description.abstract© 2014 IEEE. Risk assessment is crucial to the increase of software development project success. Current risk assessment approaches provide only a rough guide. Risk assessment experts and domain experts are required in conducting risk assessments in software projects. Therefore, traditional risk assessment approaches require extra activities besides development tasks, and possibly leading to extra costs. We believe that an effective risk assessment approach should be transparently embedded in software development process. This paper aims to present an automated risk assessment framework using CMMI and risk taxnomy as a guidance to develop a risk assessment model. A pragmatic approach will be applied as a basis in building this suggested risk prediction model and the case studies of our practice. These studies are considered as our proof of concept.en_US
dc.identifier.citationProceedings - Asia-Pacific Software Engineering Conference, APSEC. Vol.2, (2014), 63-68en_US
dc.identifier.doi10.1109/APSEC.2014.95en_US
dc.identifier.issn15301362en_US
dc.identifier.other2-s2.0-84951787927en_US
dc.identifier.urihttps://repository.li.mahidol.ac.th/handle/123456789/33729
dc.rightsMahidol Universityen_US
dc.rights.holderSCOPUSen_US
dc.source.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84951787927&origin=inwarden_US
dc.subjectComputer Scienceen_US
dc.titleA CMMI-based automated risk assessment frameworken_US
dc.typeConference Paperen_US
dspace.entity.typePublication
mu.datasource.scopushttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84951787927&origin=inwarden_US

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