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Many techniques are used for cost, quality and schedule estimation in the context of software risk management. Application of Bayesian Belief Networks (BBN) in this area permits process metrics and product metrics (static code metrics) to be considered in a causal way (i.e. each variable within the model has a cause-effect relationship with other variables) and, in addition, current observations can be used to update estimates based on historical data. However, the real situation that researchers face is that process data is often inadequately, or inappropriately, collected and organized by the development organization. In this paper, we explore if BBN could be used to predict appropriate release dates for a new set of products from a telecommunication company based on static code metrics data and limited process information collected from a earlier set of the same products. Two models are evaluated with different methods involved to analyze the available metrics data.