## 1 Introduction  
The development of technical systems is characterized by various uncertainties. Uncertainties are inevitable. They are induced internally as well as externally [1; 2]. Internally, by insufficient in-depth understanding of the system in early product development phases as a result of complexity, for instance leading to incomplete requirement sets [3]. Externally, for instance, by volatile customer needs and regulations [2]. Both can lead to changes in the requirement set. Those changes occur in all development phases and can be caused by new requirements, changed requirements or deleted requirements (Figure 1). Studies on the extent of changes indicate that more than half of all system requirements have a high risk of change throughout the project [6]. Each change may result in adaption effort and time-consuming iterations, especially if it leads to a subsequent engineering change. The impact of changes multiplies due to change propagation. Requirements as well as system elements are highly interconnected amongst each other [7]. Hence, changing one requirement often initiates a number of subsequent changes [8; 9], each resulting in further development effort due to change propagation. This is illustrated by an example from the development of an LED front headlamp for BMW [10]: The requirement for the maximum temperature of the LED substrate is a central influence factor regarding performance of the headlamp and was originally set to 120 \({}^{\circ}\)C. This was reduced to 90 \({}^{\circ}\)C in the course of development. In order to lower the temperature, more LEDs had to be installed, each with lower energy consumption. This, in turn, required to change the requirements on installation space. Overall, the initial requirement change caused change propagation towards requirements for energy consumption, number of LEDs and geometry. Managing such changes in an efficient manner is one of the biggest challenges in development projects [6; 11; 12]. The aim of the contribution at hand is to open up a new action space for requirement change management in research and industrial practice, by providing the basis for systematic risk management. Today, requirement changes are often managed in a reactive and unsystematic manner in industrial practice; changes are based on subjective judgement instead of objective data [13; 14]. This is confirmed by various studies [6; 15; 16; 17; 18; 19]: Inefficiencies by requirement changes are one of the most important causes for development projects to miss project objectives (quality, cost and time). Despite those findings, there is little research on how to manage requirement changes efficiently [6; 20]. Contributions mostly come from Requirements Change Management (RCM) [20] or Engineering Change Management (ECM) [21]. From a time perspective, approaches can be divided into three categories: Pre-change, in-change and post-change [21]. Established approaches focus on decision making (in-change) and change implementation (post-change) [22; 23; 20]. Especially in the context of risk management for requirement changes (pre-change) very few approaches exist [1; 6; 13]. Risk management may unlock a new scope of action (for instance, change prevention or reduction of change impact). Therefore, it yields large potential in the efficient management of requirement changes, as it does in other application fields. To enable risk management, the risk of a requirement changes need to be assessable as a prerequisite. Then, based on the specific risk characteristics, targeted risk management actions can be initiated. So far, the risk of requirement changes is either not assessed at all or comes from an unsystematic expert estimation with no differentiation of risk factors (for instance, drivers of change likelihood and impact). Therefore, the selection of proper actions is vague and based on insufficient information.