## 3 Method for Proactive Management of Requirement Changes
### Change Risk Assessment
#### 3.2.1 Requirement Dependency Analysis  
To find the most promising approach for requirement dependency analysis, different alternatives are tested. First, a manual approach was used as part of the preliminary study. A key disadvantage of manual approaches is an exponential growth of application effort per requirement due to pairwise analysis procedure. This makes them unsuitable for requirement sets with about 30 requirements or more. To solve this issue, requirements were assigned to categories (for instance, main features [47]), for which generic dependencies are identified beforehand. Although the exponential growth is removed, evaluation with industry experts still includes criticism of application effort for assignment of each requirement to a category. The generalization of this approach led to a reduced accuracy of results [14]. The second approach tested was based on an ontology. As proposed by [48], requirements are assigned to ontology elements, which are defined and connected by experts in advance. Assignment can be carried out manually or automatically, for example by means of machine learning. Manual assignment has equal disadvantages as the first approach. Automated assignment solves the disadvantage and seemed promising. However, discussion with industry experts indicated, that the experts' insufficient system understanding in early development stages limits the completeness and accuracy of the ontology itself. To identify a different approach, a literature study was conducted on potential solution classes [49]. Considering previous findings, a natural language processing approach based on machine learning techniques for dependency analysis was selected as a third approach to be tested. This enables a fully automated dependency analysis without expert knowledge needed. Since semantic similarity analysis (for instance [10]) might miss dependencies (for instance, synonyms or physical effects) and is not able to differentiate dependency types (indicator for propagation behavior), an approach for content-based dependency analysis is used. Bidirectional Encoder Representations from Transformers (BERT) learns from contextual relationships of words within text corpuses and enables such content-based \begin{table}
\begin{tabular}{l l} \hline
**Input** & **Application** \\ \hline SC-1: Processability of interdisciplinary requirements & SC-8: Easy to use \\ \hline SC-2: Processability of high number of requirements & SC-9: Availability of required software \\ \hline SC-3: Processability of regular form of documentation & SC-10: Early availability of \\  & required information \\ \hline
**Method** & SC-10: Generic applicability \\ \hline SC-4: Analysis of change propagation & SC-11: Acceptable application effort \\ \hline SC-5: Analysis of exogenous and endogenous & SC-12: Generic applicability \\ change likelihood & \\ \hline SC-6: Analysis of collective change impact & **Output** \\ \hline SC-7: Selection of requirement specific & SC-13: Reusability of database \\ proactive measures & \\ \hline \hline \end{tabular}
\begin{tabular}{l l} \hline _SC = Success Criteria_ & SC-14: Sufficient accuracy for intended \\  & use cases \\ \hline \end{tabular}
\end{table} 
Table 1: Success criteria (SC). analysis as well as a differentiation of dependency types [50]. To assess performance of BERT, it was compared with the following approaches commonly used as performance benchmarks: Multinomial Logistic Regression (MLR) and Support Vector Machine (SVM) as well as Recurrent Neural Network (RNN). As a database for comparison, the case study data from intelligent articulated robot arm was used (80/20 training to validation set). Criteria for comparison are Precision, Recall, F1-Score and the Receiver Operating Characteristic Area Under the Curve (ROC_AUC). Precision (positive predictive value) near 1 indicate reliable models. Recall (true positive rate) close to 1 indicate the recognition of relevant elements. The F1-Score is the harmonic mean of precision and recall.