2016
DOI: 10.14257/ijseia.2016.10.2.02
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Abstract: Techniques and models for mitigating risk in software development projects classified into three categories-namely, qualitative, quantitative, and intelligent approaches. This paper is to review the quantitative and intelligent risk models in software risk management for software development projects. Indeed, this area needs more effort from scholars and researchers in quantitative and intelligent risk models to mitigate risks. As future work, we will use these hybrid models of quantitative and intelligent for… Show more

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Cited by 3 publications
(2 citation statements)
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“…The authors also establish the main risk factors and types of technical risks that occur in the different stages of the SDLC (requirements, planning, design, building of the code/implementation, deployment and maintenance), identifying, moreover, what risks are associated with the scheduling or calendar, the costs, the quality and the business. For their part, Elzamly et al [44] published a literature review whose purpose was to identify which quantitative and intelligent models are used for risk management, apart from the techniques associated with each one of these models for risk management in software development projects. The researchers conclude that in the future it would be possible to work to design hybrid models that combine both types of models, incorporating different artificial intelligence techniques to mitigate risk in cloud computing and in banking transactions.…”
Section: Related Workmentioning
confidence: 99%
See 1 more Smart Citation
“…The authors also establish the main risk factors and types of technical risks that occur in the different stages of the SDLC (requirements, planning, design, building of the code/implementation, deployment and maintenance), identifying, moreover, what risks are associated with the scheduling or calendar, the costs, the quality and the business. For their part, Elzamly et al [44] published a literature review whose purpose was to identify which quantitative and intelligent models are used for risk management, apart from the techniques associated with each one of these models for risk management in software development projects. The researchers conclude that in the future it would be possible to work to design hybrid models that combine both types of models, incorporating different artificial intelligence techniques to mitigate risk in cloud computing and in banking transactions.…”
Section: Related Workmentioning
confidence: 99%
“…In a nutshell, it is evident that the work mentioned above has focused mainly on (i) Risk identification [31,[35][36][37], as well as on strategies for risk mitigation in GSD [31]; (ii) identification of the challenges that have an impact on risk assessment and the establishing of what particular risk management strategies are used in DSD in order to support decisions (DSS) [38]; (iii) identification and listing of the risks in software product lines (SPL), while also establishing what their practices and management strategies are [39]; (iv) identification and classification of risks in software projects based on components (Offthe-Shelf, OTS) [40]; (v) identification of the use of modelling for the simulation of software processes (SPSM) [12]; (vi) identification and analysis of risk management practices at the level of the literature [10,44], as well as at the software company level [41][42][43].…”
Section: Related Workmentioning
confidence: 99%