## 3 Method for Proactive Management of Requirement Changes
### Change Risk Assessment
#### 3.2.1 Requirement Dependency Analysis
It measures the extent to which the classes are distributed in a balanced manner and indicates accuracy of the model. ROC_AUC measures performance of a classifier and observes the relationship between true positive rate and false positive rate. This metric is often used to evaluate unbalanced data. Values close to 1 are considered reliable [51]. To ensure significance of the results, comparison criteria needed to be viewed macro averaged (equal weight on all classes). This is necessary since the dataset is unbalanced and has a major class (\"none\") and several minor classes (dependency types such as \"requires\"). Using random oversampling and class weighted as standard data augmentation approaches, BERT outperforms the other models (cf. Table 2). Precision, Recall and F1 values of BERT are good in terms of a multi-class classification problem and ROC_AUC is close to the optimum. Therefore, BERT is used for dependency analysis (cf. Figure 3). Before dependency analysis itself can be conducted, data preprocessing is required to transform requirement data into the required form (vectors). Then, classification of requirement dependencies is implemented by means of a BERT model. The model is based on a generic pretrained model and fine-tuned for the intended task. As a last step, the dependency data are transformed into common data formats (for instance, csv files) for further usage. is to train the model to identify contextual related text data [52]. Pre-trained models are generic and can be used from open source databases (in this case hugging [53]). After pre-training, the model is fine-tuned to the individual task and context. For dependency analysis, a classification layer is put on top of the transformer output, to enable BERT to determine predefined (dependency) classes. Fine-tuning is carried out in a supervised manner with labelled text dataset (case study data on intelligent articulated robot arm). The same as for comparison, requirement data were artificially enhanced using random selection and weighted by classes for data augmentation to compensate imbalances in the dataset. After fine-tuning, a task and context specific BERT model is usable for dependency analysis. To transform requirement data into a suitable data format for dependency analysis, data preprocessing is required. Input data (textual requirement descriptions) need to be cleaned and transformed into a mathematical representation (vectors). First, requirements are formed into an ongoing text of two input requirements (text field) added by a dummy label (label field). The dummy labels will later be changed into a dependency type. Then, requirement text is tokenized--splitting the sentence into single words and categorizing them (stop word vs. content word). To do so, the BERT tokenizer was used [54]. Finally, the text data can be transformed into a vector representation and used as input for the BERT model. Classification of Dependencies  
To identify dependencies, the preprocessed input data are analyzed with fine-tuned BERT model. Its task is to determine the estimated dependency type between two requirements. To indicate the propagation behavior, the dependency types \"refines\", \"refined by\", \"requires\" and \"required\" are significant [55]. For this reason, these four types are classified. Classification is carried out fully automated. The result is a dataset of the requirement text and their estimated dependency type. Transformation of Results  
For further usage of dependency information, the output of the BERT model needs to be transformed into a suitable data format--such as xls or ReqIF. The most suitable data format is dependent from the intended use. In this case, a list of the requirements and their classified dependency type is created in xls data format, which can be used for impact assessment and change management.