## 3 Related Studies  
From reviewing the literature pertaining to the crowd selection aspect for crowdsourcing RE tasks, to the best of our knowledge, the studies discussed in this section are the ones that have investigated this specific research topic. The studies have utilized a variety of techniques to address this problem and each is discussed in this section. The StakeNet approach presented by [PERSON] et al. [59] is a stakeholder analysis approach for extensive software projects which could comprise a crowd of thousands of stakeholders. It considers relations between stakeholders to identify and prioritize them according to their salience and roles. This is achieved by asking stakeholders to suggest other stakeholders, generating a social network of stakeholders. The network is then analyzed using various social network measures (e.g., betweenness centrality, closeness centrality). [PERSON] et al. [55] further improved StakeNet by proposing StakeSource that leverages crowdsourcing for stakeholder analysis. A web-based tool was built to automatically conduct the process. Hence, to curtail the amount of work for experts when conducting stakeholder analysis, the process is crowdsourced to the large base of stakeholders. Moreover, StakeSource [58] was enhanced in StakeSource2.0 which utilizes crowdsourcing, social network analysis and collaborative filtering for stakeholders' identification and prioritization and extends this by further considering their requirements. Crowdsourcing enables all stakeholders to have their opinions considered which increases requirement completeness. Furthermore, [PERSON] and [PERSON] proposed StakeRare [56] which employs StakeSource2.0 to identify and prioritize the crowd of stakeholders and their requirements. The method was evaluated on a 30,000-stakeholder system and was proven to be capable of accurately identifying and prioritizing requirements. A study conducted by [PERSON] et al. [57] has found an in-group bias limitation in the previous methods which means that stake holders normally tend towards suggesting stakeholders whom they have a good relation with. This defined issue can impact the accuracy when identifying and prioritizing the crowd of stakeholders. Therefore, they have contributed a social-network-based approach which utilizes the hybrid centrality measure (HCM) technique combined with the power, legitimacy and urgency (PLU) technique. Through a controlled experiment, the approach has proved its effectiveness in reducing the bias issue. The previously discussed studies have focused on the connections within the crowd of stakeholders and the level of impact they have on a software project when selecting a crowd using social network measures. However, other studies have stressed their focus on the crowd's domain knowledge to select a suitable subset of the crowd. An example is [PERSON] et al. 's opportunistic participant recruitment framework [7] that selects a crowd of participants to get involved in requirements acquisition tasks for mobile apps. They have stated that a crowd assembled at the same time and in the same space are most likely to possess the same domain knowledge. Therefore, the spatiotemporal data of the crowd were harnessed to invite them to participate in the crowdsourced task. The task is distributed using AirDrop to close participants and further spread to a wider range of participants. Furthermore, another study conducted by [PERSON] and [PERSON] [5] has considered domain knowledge to select a suitable subset of the crowd from a professional social network called LinkedIn. The authors have focused on selecting a crowd with expertise and knowledge in ERP to crowdsource the task of requirement elicitation for the MyERP software application. Hence, they have built a crawling tool that can target users from LinkedIn that have ERP added as one of their skills in their profiles. Additionally, a study by [PERSON] et al. [64] has leveraged LinkedIn as source for a crowd of hidden stakeholders of B2B enterprise software.