## 5 Discussion and Conclusions  
For analysis of change propagation, the evaluation shows that the BERT approach produces the best results. Dependencies are detected with a high performance (F1 macro avg. 96%). Up to 4250 requirements can be processed within 5 h when using a cloud server for running the dependency analysis. The analysis of exogenous and endogenous change likelihood is assessed by experts as having an acceptable application effort and all required information is available in an industrial context. The change impact analysis is rated sufficiently accurate by industry users. Additionally, relying on expert knowledge as part of the analysis is confirmed to be practical. An improvement potential is seen in the context of training data for dependency analysis. Manually creating a sufficient database is time-consuming and new ways to generate such training data should be investigated. Overall, pilot users' feedback shows that all method prerequisites are given in an industrial context (e.g., availability of required input-data and information), the method is easy to apply and provides a useful support. Evaluation results may be limited due to the number of case studies and industry experts involved. To overcome these deficits, method and software prototype need to be tested and discussed in a broader context. As an initial step, the results have been introduced to fifteen industry partners and three research partners in the context of the two follow-up research projects ImPaKT and BIKINI. Exchange with experts shows high interest in the ProMaRC method and dependency analysis in particular. Experts also confirmed evaluation results in two workshops with up to 15 practitioners and ongoing exchange. The presented ProMaRC method contributes to the demand for proactive requirement change management [2; 5; 6; 8; 14] and practice [1; 4; 16]. It opens up a new way to reduce the risk of project failure by means of requirements change risks assessment (RQ1) and management (RQ2) in the early development stages of complex technical systems. Additionally, it gives a framework for existing approaches of ECM [8; 9; 23; 33; 34; 88; 89] and RCM [5; 10; 13; 31; 48; 90] to be used for proactive risk management. In the field of dependency analysis, the ProMaRC method demonstrates application potentials for transformer-based machine learning technique BERT in requirements engineering. Future research directions are: (1) improvement of the ProMaRC method, (2) integration into the broader context (development processes and model-based systems engineering) and (3) extensive evaluation. Improvement of the ProMaRC method can be carried out in the context of dependency analysis and impact assessment. For dependency analysis, a more efficient way to create training data or train the BERT model needs to be developed. Experts are not willing to create large datasets for training manually and data augmentation techniques applied (for instance random oversampling) might lead to overfitted dependency models. Furthermore, transferability of dependency models into new development contexts (for instance, different branch or product) needs to be improved in terms of analysis performance. Transferability is key to enable broad usage in an industrial context and reduces application effort for single projects. For impact assessment, the impact evolvement (for instance sudden burst or rising [71]) can be considered to recommend more suitable proactive measures. Integrating the ProMaRC method into the broader development context is essential for long-term embodiment of proactive change management. The method needs to be integrated into established development approaches (for instance systems engineering). Future research also needs to investigate the linkage of the ProMaRC method towards model-based systems engineering (MBSE). MBSE aims to represent the interplay of requirements, functions, logical solution elements and physical solution elements by means of models.