Data-driven Risk Management for Requirements Engineering: An Automated Approach based on Bayesian Networks
This addresses risk management in Requirements Engineering for software projects, offering an automated approach to reduce reliance on expert experience, though it is incremental as it builds on existing survey data and Bayesian methods.
The authors tackled the challenge of determining how much Requirements Engineering (RE) is needed and which methods to apply by training Bayesian Networks on NaPiRE survey data to model relationships between RE problems, causes, and effects, enabling diagnostic and predictive analyses with rigorous cross-validation.
Requirements Engineering (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 postmortem (diagnostic) analysis, deriving probable causes of suboptimal 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