Abstract

Requirement changes and cascading effects of change propagation are major sources of inefficiencies in product development and increase the risk of project failure. Proactive change management of requirement changes yields the potential to handle such changes efficiently. A systematic approach is required for proactive change management to assess and reduce the risk of a requirement change with appropriate effort in industrial application. Within the paper at hand, a novel method for Proactive Management of Requirement Changes (ProMaRC) is presented. It is developed in close collaboration with industry experts and evaluated based on workshops, pilot users’ feedback, three industrial case studies from the automotive industry and five development projects from research. To limit the application effort, an automated approach for dependency analysis based on the machine learning technique BERT and semi-automated assessment of change likelihood and impact using a modified PageRank algorithm is developed. Applying the method, the risks of requirement changes are assessed systematically and reduced by means of proactive change measures. Evaluation shows high performance of dependency analysis and confirms the applicability and usefulness of the method. This contribution opens up the research space of proactive risk management for requirement changes which is currently almost unexploited. It enables more efficient product development.

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