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Design and Development of Machine Learning Technique for Software Project Risk Assessment - A Review

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dc.citedby3
dc.contributor.authorMahdi M.N.en_US
dc.contributor.authorM.h M.Z.en_US
dc.contributor.authorYusof A.en_US
dc.contributor.authorCheng L.K.en_US
dc.contributor.authorMohd Azmi M.S.en_US
dc.contributor.authorAhmad A.R.en_US
dc.contributor.authorid56727803900en_US
dc.contributor.authorid35185866500en_US
dc.contributor.authorid35185858900en_US
dc.contributor.authorid57188850203en_US
dc.contributor.authorid36994351200en_US
dc.contributor.authorid35589598800en_US
dc.date.accessioned2023-05-29T08:08:01Z
dc.date.available2023-05-29T08:08:01Z
dc.date.issued2020
dc.descriptionMachine learning; Project management; Software design; Design and Development; Development performance; Literature reviews; Machine learning approaches; Machine learning techniques; Size metrics; Software project; Software project risks; Risk assessmenten_US
dc.description.abstractAccurate assessment of software project risk is amongst the key activities in a software project. It directly impacts the time and cost of software projects. This paper presents a literature review of designing developing machine learning techniques for software project risk assessment. The results of the review have concluded prominent trends of machine learning approaches, size metrics, and study findings in the growth and advancement of machine learning in project management. Besides that, this research provides a deeper insight and an important framework for future work in the software project risk assessment. Furthermore, we demonstrated that the assessment of project risk using machine-learning is more efficient in reducing a project's fault. It also increases the probability for the software project's prediction and response, provides a further way to reduce the probability chances of failure effectively and to increase the software development performance ratio. � 2020 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo9243459
dc.identifier.doi10.1109/ICIMU49871.2020.9243459
dc.identifier.epage362
dc.identifier.scopus2-s2.0-85097651183
dc.identifier.spage354
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85097651183&doi=10.1109%2fICIMU49871.2020.9243459&partnerID=40&md5=c2ad510cb54f6955fa1f215b6608c7e6
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25308
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitle2020 8th International Conference on Information Technology and Multimedia, ICIMU 2020
dc.titleDesign and Development of Machine Learning Technique for Software Project Risk Assessment - A Reviewen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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