## 4 Results
### Method for Systematic Assessment of Requirement Change Risk
#### 4.1.2. Consideration of Change Propagation Impact and Endogenous Change Likelihood  
Despite change impact and exogenous change likelihood, endogenous factors such as endogenous change likelihood and the impact of change propagation have to be considered in assessing the risk of a requirement change. Based on the requirements dependency model proposed by [PERSON] [58], a matrix based-approach based on a modified DSM--the Requirements Structure Matrix (RSM) was used (for detail, see [59]). To enhance the practical applicability of the method especially in SMEs, an automatized assessment for requirement interrelations was applied (see [51]). Requirement interrelations are currently not tracked due to a shortcoming in application of RE tools in SMEs and use of such information (Section 1). By applying the semi-automatized approach, the effort for assessing requirement interrelations can be reduced significantly, compared to a manual pairwise assessment. In order to assess requirement interrelations semi-automatically by applying artificial neural networks, machine learning or ontology-based Artificial Intelligence (AI) approaches, the required number of samples has to be high enough to ensure precision. When it comes to product development projects especially in SMEs, the number of samples is relatively low. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Criterion** & & **Reflexive Questions** \\ \hline U & Uncertainty & 1. & Can we precisely specify or quantify the requirements related to this influence factor? \\  & & 2. & Can we assume that the requirements related to this influence factor are fully known? (implicit vs. explicit requirements). \\  & & 3. & How familiar are requirements related to this influence factor and how precisely can we assess the statements (needs/interests)? (new vs. existing customer; anonymous customer). \\  & & 4. & Are the requirements related to this influence factor company-specific or standard requirements in the industry? \\ \hline D & Dynamics & 1. & How often, in our estimation, will there be changes to requirements related to this influence factor? (Frequency/constancy of requirements). \\  & & 2. & When do changes in requirements related to this influence factor usually become known? (immediately vs. delayed). \\  & & 3. & When do changes of requirements related to this influence factor occur in the course of the project? \\  & & 4. & Can we influence changes in the requirements related to this influence factor? \\  & & 5. & How severe are the changes of requirements related to this influence factor? \\ \hline R & Relevance & 1. & How critical to success are the requirements of this sphere of influence? \\  & & 2. & Are the changes in the requirements of this sphere of influence feasible? \\  & & 3. & Is it possible to limit the effects of changes in the requirements of this sphere of influence? \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Reflexive questions. Therefore, instead of using a large number of samples and AI, the semi-automatic assessment of requirement interrelations is based on a formalized rule set [51]. To ensure broad applicability and transferability as well as comprehensiveness [60], the main feature list developed by [PERSON] and [PERSON] [61] was chosen as a basis for the rule set and tailored towards the project context. In a first step, requirements were elicited using the main feature list and documented in a requirements list. Within this list, the related main feature was assigned to each requirement. Interrelations among these main features and further context specific features (i.e., hygienic design in case of case study 2 \"control cam\") were assessed by experts on the scale 0, 1 and 2. \"0\" represented no interrelation, \"1\" a weak interrelation and \"2\" a strong interrelation between requirements related to the main features.