## 1 Introduction
To exploit the potential of risk management in the efficient handling of requirement changes, a major challenge is to provide a support for systematic assessment of requirement change risks. Figure 1: Inevitability of requirement changes [4; 5]. The second challenge comes from the wide-spread phenomena of insufficient management of requirement change in practice [24; 25; 26]. The challenge is to reach low application effort of risk assessment in order to increase usability in practice. Today, key areas of research in RCM und ECM are dependency analysis and impact analysis. Both are essential for the assessment of change propagation as well as the decision-making and change implementation. Furthermore, the dependency and impact analysis cause a major proportion of application effort and therefore play a critical role in industrial application. Interviews with practitioners also show that those activities are major barriers to systematic change management in general, due to a lack of methodical support, cross-disciplinary system understanding and problem awareness regarding change effects. As a result, the perceived cost-benefit ratio of systematic dependency and impact analysis is negative. The interviewees were eleven experts from the industrial partners of the OptiAMix project (Section 3). Three of them work as developers and eight interviewees are acting in leading positions in product development (for instance, chief engineers, senior development experts or project leaders). Research on ECM has mechanical engineering background and focuses on the solution space [21]. ECM approaches support the management of changes to engineering elements, for instance, by analysis of physical dependencies between those elements (for instance [27]). Since the number of (physically) interconnected engineering elements are usually not as high as the number of interconnected system requirements, those approaches more often rely on expert evaluation [20; 21]. Anyway, the reduction of application effort and therefore the development of (semi-) automated approaches gains relevance, especially to support dependency analysis (for instance [19; 28; 29]). Research in RCM mostly comes from software development [20]. Software development projects face high numbers of system requirements and therefore, automation is a key challenge. Several approaches with a high degree of automation exist [30; 31], but rely on structured and software-specific data like source code or Unified Modeling Language (UML) diagrams (for instance [32; 33]) for dependency analysis. Therefore, the application in the development of (partly) mechanical systems is limited. Approaches from both research fields have in common that they predominantly rely on Machine Learning (ML) or expert assessment. Both have unique characteristics that demand for a context specific selection. ML requires large, structured datasets, especially if the analysis consists of multiple parameters (for instance dependency types). Such datasets often do not exist due to a prevalent lack of systematic, document-based requirements engineering in technical development projects [26], which applies in particular to Small and Medium-sized Enterprises (SME). In case appropriate datasets exist, they usually come from requirement change databases of requirements engineering tools like IBM DOORS (for instance [34]). Those can be valuable sources, but the findings may lack of transferability to other development contexts (system, company or branch) and transparency in reasoning. The advantage of ML is that there is very little application effort and no expert knowledge needed after the training phase [35]. Expert assessment has the opposite advantages and disadvantages. Experts' ability to abstract and express knowledge increases transferability and transparency. The most severe disadvantage is its high application effort and dependence on expert knowledge. The application effort increases with the amount of estimations needed.