## 1 Introduction
Additionally, the assessment of the direct change impact is unaided and based on expert assessment, which implies subjectivity of results and further application effort. The change prediction method (CPM) is well established in ECM and builds the basis for several other approaches. The CPM itself is focused on propagation effects between components and solely relies on manual expert assessment [9]. Advances to reduce subjectivity of assessment and include requirements such as [8] increase the accuracy of the results and suitability for requirement change management, but still lack applicability for extensive requirement sets. This issue is addressed by automated approaches based on dependency models [5; 10; 35; 36; 37] or historical change data [23]. For those approaches, the availability of required information is limited, since dependency models of requirements are missing in the early stages or need to be created by experts. Additionally, there are no sufficient databases on historical requirement changes that contain information on change propagation and impact [14]. Proactive Measures to manage the requirements' change risk are introduced by risk management literature in the first place. They can be structured by category (avoidance, transfer, reduction and acceptance [27]) and aim to reduce the likelihood or impact of a potential requirement change. Existing approaches provide methods for individual elaboration of appropriate measures (for example, creativity techniques such as brainstorming [38] or Six Sigma DMAIC cycle [28]) or list generic risk measures (for instance [27; 39; 40]). Neither a support to select appropriate measure based on the present requirement risk characteristics nor an overview of context specific risk measures for requirement changes are given. The lack of research in proactive management of requirement changes also becomes visible in established requirements engineering software products such as IBM DOORS or Siemens Polarion Requirements. Existing functionality enables modeling requirement dependencies and track changes manually (traceability). They do not support automated dependency detection besides inconsistency analysis, capturing of change risk data or the selection of proactive measures. Looking at Microsoft Office, which is often used for requirements engineering in small and medium-sized enterprises [21], the deficit is even more severe, since it does not support dependency analysis and change risk assessment at all. Overall, there is a lack of research in the following areas:  
* Methods for holistic assessment of requirement change likelihood (exogenous and endogenous) and change impact (direct and subsequent). * Approaches to reduce the application effort of risk assessment without relying on datasets, which are unavailable in the context of proactive requirement change management (especially for dependency analysis). * Support to select proactive measures based on the requirement change risk. On that account, the contribution at hand aims to answer the following two research questions (RQ):  
* RQ 1: How can the change risk of requirements be assessed in the early stages of developing complex technical systems? * RQ 1.1: How can requirement dependencies be identified and differentiated by change propagation behavior? * RQ 1.2: How can the endogenous and exogenous change likelihood of a requirement be assessed? * RQ 1.3: How can the collective change impact be assessed? * RQ 2: How can the selection of proactive measures for requirement changes be supported? The article is structured as follows: Section 2 presents the research design. The method for proactive change management of requirements and the according software prototype are explained in Section 3. In Section 4, evaluation of the method is carried out. Finally, the results are discussed in Section 5 and an outlook is given.