Influence diagrams have been used effectively in applied decision analysis to model complex systems, identifi probabilistic dependence and characterize the flow of information. We argue that influence diagrams are a natural Pt for decision making for software risk analysis. Our approach is vety general and can be adapted for various type of software risks. We use software metrics collected at various stages of software development. The metrics data is analyzed by an influence diagram to identi& the sources of errors and predict future risk items like reliability, complexity, schedule, or non-reusability.
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