Wiesweg, FlorianVogelsang, AndreasMendez, DanielKoziolek, AnneSchaefer, InaSeidl, Christoph2020-12-172020-12-172021978-3-88579-704-3https://dl.gi.de/handle/20.500.12116/34544This paper has been accepted at the 2020 IEEE Requirements Engineering Conference (RE). RE is a means to reduce the risk of delivering a product that does not fulfill the stakeholders' needs. Therefore, a major challenge in RE is to decide how much RE is needed and what RE methods to apply. The quality of such decisions is strongly based on the RE expert's experience and expertise in carefully analyzing the context and current state of a project. Recent work, however, shows that lack of experience and qualification are common causes for problems in RE. We trained a series of Bayesian Networks on data from the NaPiRE survey to model relationships between RE problems, their causes, and effects in projects with different contextual characteristics. These models were used to conduct (1) a post-mortem (diagnostic) analysis, deriving probable causes of sub-optimal RE performance, and (2) to conduct a preventive analysis, predicting probable issues a young project might encounter. The method was subject to a rigorous cross-validation procedure for both use cases before assessing its applicability to real-world scenarios with a case study.enRequirements EngineeringData-Driven RERisk ManagementText/ConferencePaper10.18420/SE2021_471617-5468