## 6 Evaluation Experiment of the Proposed Approach
### Comparing Our Findings to Related Studies  
Our crowd selection approach aligns with the ones proposed by related studies in terms of the main objective which is choosing a suitable crowd to contribute to crowdsourcing RE tasks. The related studies were diverse in terms of the techniques they have used for crowd selection, which differ from the ones we have used for our crowd selection approach. Therefore, we qualitatively compare our findings to those of the related studies. One of the noteworthy properties is that our crowd selection approach does not consider social connections between crowd members such as in studies [55; 56; 57; 58; 59]. It was perceived that it is highly possible to find the best-suited subset of the mass crowd who are not necessarily connected and would contribute valuably when participating in a crowdsourced RE task. Moreover, a component of our approach is mobile app user and developer classification models to identify mobile app users and developers on Twitter. This work is considerably similar to the models developed by [PERSON] et al. [62]. Our models have mainly focused on mobile apps, where [PERSON] et al. 's were more general, considering desktop and mobile apps. Furthermore, the results revealed that our models managed to achieve higher results than their stakeholder classification model. Their model can identify three types of stakeholders from tweets, where the technical stakeholder classifier precision was 0.54, non-technical stakeholder classifier precision was 0.77 and general public classifier precision was 0.78. On the other hand, we have managed to achieve a precision equal to 0.86 for identifying mobile app users and 0.88 for identifying mobile app developers. In addition, our proposed approach considers not only the role of the crowd--user or developer--it was also focused on the application domain knowledge they have gained by using or developing apps. Their domain knowledge is deduced by analyzing the textual descriptions of the apps they used or developed. We believe this method can infer pivotal details that can assist in recognizing the domain knowledge possessed by the crowd compared to the approach followed by [PERSON] et al. [7]. The latter approach considers spatiotemporal information of the crowd to deduce their domain knowledge which can overlook important information. Furthermore, our approach is customizable, by which it does not involve only a single type of software domain. [PERSON] and [PERSON]'s approach [5] selects a crowd with expertise in ERP software, and [PERSON] and [PERSON] [61] focused on a crowd interested in a certain domain. Our approach addresses this gap by supporting all types of mobile app domains by designing an approach where the selection process can be customized according to the required mobile app domain by measuring the semantic similarity between apps' descriptions. From our controlled experiment, the assessment results of the crowdsourced requirements elicited from a selected crowd have demonstrated that our approach can select a suitable crowd for crowdsourcing a mobile app in a specific domain. Additionally, our work has utilized a combination of quantitative and qualitative methods for evaluating the approach, while not all related work has conducted a mixed evaluation method. The components have been quantitively evaluated separately using the appropriate metrics, and the complete approach was qualitatively evaluated through a controlled experiment involving a real crowd from Twitter. Compared to the reviewed related work, our work has considered five main quality factors for assessing the quality of the crowdsourced requirements. These factors have been selectively chosen due to their suitability in the case of crowdsourcing RE, rather than being factors that are usually used for assessing requirements when applying conventional RE. In addition, our research has assessed each of the crowdsourced requirements separately in terms of the five previously mentioned quality factors.