## 1 Introduction
To do so, a support to assess the change risk, comprising change likelihood and impact, as well as a systematic approach to select proactive measures is needed. On the methodical level, areas of relevance are change management and risk management in product development. Change management approaches [24,25] and guidelines (for instance [26]) support the decision making on change requests and a systematic change implementation, but lack a consideration of pre-change stages decisive for proactive management [14]. Approaches for risk management in product development focus on such pre-change stages, but remain generic for development risks in general [6,27,28] or focus on other risks than requirement changes [29]. Therefore, a support is missing to open up requirement change management approaches towards pre-change stages and gives context specific support for proactive requirement change management. Figure 1: Potential of proactive requirement change management to reduce negative change effects. Looking at the detailed level, areas of relevance for change risk assessment and proactive management are engineering change management (ECM) and requirements change management (RCM). ECM originates from a mechanical engineering background with primary focus on component changes [30], whereas RCM is dominated by approaches on software engineering [31]. Existing approaches are structured by their contribution towards the following three dimensions: requirement change likelihood and change impact (both compose the change risk [32]) as well as proactive measures to manage the change risk. Within each dimension, the approaches are differentiated regarding (1) availability of requirement information to ensure applicability in early development stages and (2) applicability for extensive interdisciplinary requirement sets, especially looking on application effort and usability for interdisciplinary requirements. Those two aspects represent major constraints of the intended application context: early stages of developing complex technical systems [14]. The assessment of requirement change likelihood needs to include change triggers that initiate a change (for instance, volatile customer needs) as well as propagation effects within the requirement network. Initial changes are triggered by factors outside the requirement set (exogenous change likelihood), whereas propagation effects occur between requirements and only occur after an initial change (endogenous change likelihood) [14]. While approaches such as [5; 6; 9; 23; 33] assess propagation effects, initial change triggers are not taken into account. Propagation analysis is carried out manually or automatically. Approaches with automated analysis enable application on extensive requirement sets with reasonable effort, but lack the availability of requirement information in context of requirement changes [23] or differentiation of requirement dependencies by propagation behavior (for instance, by dependency type [5; 33]) to ensure accuracy of results. Through use of expert evaluation, the required information is available for manual approaches such as [6; 9] to the extent of the present system understanding. System understanding includes evaluation of propagation behavior. The disadvantage of manual approaches lies in the application effort. Since an evaluation of each requirement pair is required for dependency analysis, manual approaches become impractical for extensive requirement sets [5]. Requirement change impact assessment is influenced by the direct impact of the initial requirement change as well as subsequent impacts from change propagation. A consideration of both is required to assess the collective change impact. This is only carried out by [34], but this approach requires a large database with pairwise requirement analysis to perform Monte Carlo Simulation. Although the analysis itself is automated, creation of the database is equally effortful as manual likelihood assessment and therefore not suitable for extensive requirement sets.