## 1 Introduction
For dependency analysis, many approaches rely on pairwise comparison between requirements to determine the existence and type of dependencies [24; 36; 37]. This leads to an exponential growth in application effort in relation to the number of requirements. Thus, its application usability is limited to small requirement sets. Quality of expert assessment strongly relies on expert knowledge and experiences. Therefore, it is subjective and biased. This reduces comparability and validity of results, especially in interdisciplinary contexts when diverse knowledge is needed [34]. Although there are approved actions to reduce subjectivity like group assessments, those lead to even higher application effort. Approaches that combine ML and expert assessment potentially dissolve the before mentioned disadvantages. However, a systematic literature indicates that no approaches exist in the context of risk management for requirement change [35]. They transform expert knowledge into structured, machine-readable data (for instance by ontologies), which can subsequently be joint with ML algorithms (for instance [31]). Established requirements engineering software products like IBM DOORS or Siemens Polarion Requirements also do not provide sufficient support in risk management of requirement changes. Those tools enable to model requirement dependencies and track changes manually (traceability). However, they do not include functions for automated dependency detection besides inconsistency analysis. Therefore, those tools facilitate expert assessment, mainly by providing user interfaces, but do not reduce application effort. Additionally, those tools are merely used by large companies. SMEs more often rely on generic software like Microsoft Office for requirements management [26] and have no support in dependency analysis and risk assessment at all. Overall, there is a lack of research in two areas: (1) the systematic assessment of requirement changes and (2) approaches to reduce application effort of risk assessment without relying on large datasets of past changes. On that account, the contribution at hand aims to answer the following two Research Questions (RQ):  
* RQ 1: How to assess the risk of requirement changes systematically? * RQ 2: How to reach an application effort for risk assessment, which is appropriate for industrial application? Answering these questions helps to reduce negative effects of requirement changes in development projects and thereby the risk of project failure [6; 38]. Due to the dynamic environment and inevitability of uncertainties in development as well as the increase in system complexity, the efficient management of requirement changes will continuously gain importance for companies to stay competitive and focus on the latest stakeholder needs. The paper at hand contributes a method for systematic assessment of requirement change risk and a software prototype that enables semi-automated application within the development context. Findings from status quo analysis with the industry partners clarifies the needs and leverage points for support in this field and evaluation proves usability and applicability of the method. Accordingly, the systematic assessment of requirement change risk provides the prerequisite for future research on risk management for requirement changes and its integration into development practices.