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Requirements Engineering (RE) is a means to reduce the risk of delivering aproduct that does not fulfill the stakeholders' needs. Therefore, a majorchallenge in RE is to decide how much RE is needed and what RE methods toapply. The quality of such decisions is strongly based on the RE expert'sexperience and expertise in carefully analyzing the context and current stateof a project. Recent work, however, shows that lack of experience andqualification are common causes for problems in RE. We trained a series ofBayesian Networks on data from the NaPiRE survey to model relationships betweenRE problems, their causes, and effects in projects with different contextualcharacteristics. These models were used to conduct (1) a postmortem(diagnostic) analysis, deriving probable causes of suboptimal RE performance,and (2) to conduct a preventive analysis, predicting probable issues a youngproject might encounter. The method was subject to a rigorous cross-validationprocedure for both use cases before assessing

Data-driven Risk Management for Requirements Engineering: An Automated Approach based on Bayesian Networks