# Data-Based Stakeholder Identification in Technical Change Management
## 7 Discussion
### Fulfillment of Requirements  
This section discusses and evaluates the requirements defined in Section 4, grouped into the two categories of \"data analysis requirements\" and \"method-oriented requirements\". An overview of the fulfillment of the requirements is presented in Table 3. With regard to the \"data analysis requirements\", it can be stated that only freely available software, in particular Python libraries, was used to support the developed tool. Therefore, R1.1 (\"open-source software\") is rated as completely fulfilled. R1.2 (\"semantic text contexts\") is also considered to be fulfilled since the HAN approach used in combination with word embeddings is focused on recognizing and mapping the hierarchical structure within documents and thus semantic contexts. In contrast, R1.3 (\"quantitative evaluation\") is considered only partially fulfilled. Although the quality of the results of the developed approach was evaluated on the basis of several established metrics (e.g., recall, precision,and F1 score), the model performance could not be compared in detail with the expert-based evaluation (e.g., detection of false positives). However, requirement R1.4 (\"prerequisite for automated analysis of new change requests\") is considered to be fulfilled. If the procedure is applied as described, the result is a classification model that evaluates new change requests by using historical data and expert knowledge. For the final application in industrial practice, suitable interfaces and data connectors should be provided in the next step. R1.5 (\"performance quality classifier\") can be considered fulfilled with respect to the particularly relevant metric of recall. It can be concluded that the developed approach can assess change requests in slightly better quality and significantly faster than an expert.  
The first \"method-orientented\" requirement R2.1 (\"availability of information for model building\") can be considered fulfilled. Existing preliminary work proves that a large majority of companies record both textual and additional change data. Thus, the broad industrial availability of the required information can be assumed. However, differences are to be expected with regard to the type and quality of the additional information collected, which is why our approach also considers data preselection. Therefore, R2.2 (\"procedure for text preprocessing\") and R2.4 (\"objective data selection\") can also be positively evaluated. The approach contains clear descriptions of the necessary steps for data preprocessing. The review or elimination of data-quality problems was not the focus of the present contribution, which is why reference should be made to established literature in this regard. The use of expert knowledge in change classification was described in detail and successfully applied twice. Therefore, R2.3 is also considered fulfilled. The obtained quality of results, as well as the efforts to implement the approach, show that industrial applicability (R2.5) was sufficiently considered during the development of the approach. Finally, R2.6 (\"adaptability\") can also be considered as completely fulfilled, as the approach supports the diversity of possible databases and companies by systematically supporting data preparation and selection, as well as the collection and integration of expert knowledge.