Risk Management in Global Software Development Process Planning
DOI: 10.1109/SEAA.2011.64 Conference: Software Engineering and Advanced Applications (SEAA), 2011 37th EUROMICRO Conference on
We present a methodology for effective risk management in global software development process planning. The proposed methodology starts with a detailed process model which is used for identifying risks based on a risk factor list. By linking the relevant risk factors to the process we seek to ease and improve the planning of the global software development process. Additionally, we built sub-processes for risk treatment. These risk treatments can be used to evaluate process improvements by simulation. Our tool-based methodology includes the following four steps: initial modeling of the original process, discovering the process risks by applying selected risk identification techniques, simulation and evaluation of process improvements, systematic selection and transformation of the original process model into an improved process model.
Available from: Huma Khan
Available from: Huma Khan
Available from: Sakda Arj-Ong Vallipakorn
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ABSTRACT: This research aimed to predict the risks in software development projects by applying multiple logistic regression. The logistic regression was used as a tool to control the software development process. These consisted of the risk stratification and causal risk factors analyses. This statistical integration was intended to establish the risk factors, anticipated and minimized the risk, which can occur during processes of software development. The factor analysis incorporated with logistic regression was used to predict the risk classification probability of failure or success of software development. The logistic regression analyses can grade and help to point out the risk factors, which were important problems in development processes. These analytical results can lead to create and development of strategies and highlighted problems, which are important issues to manage, control and reduce the risks of error. The result from classification of questionnaires of software development risk analyses by SPSS program had overall prediction accuracy at 90%.
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