# Leveraging Social Network Analysis for Crowdsourced Software Engineering Research
## 1 Introduction  
Crowdsourced software engineering (CSE) refers to the use of crowdsourcing techniques in software engineering [1, 2]. Crowdsourcing can be used in different types of software engineering activities, including requirements, design, implementation, testing, and verification. It is performed by posting an open call to which online developers can respond and be hired to work on various tasks; it is an alternative to assigning tasks to in-house developers. Different crowdsourcing platforms have been designed to support and manage CSE tasks, including TopCoder, AppStori, and uTest. CSE reduces the time spent on an activity and its cost while enhancing the quality of software development.  
Companies use crowdsourcing as an alternative to in-house development or outsourcing for different reasons. They may not have enough resources or expertise to develop a certain type of software. They may want to reduce the cost and duration of the software development process. They may also want to obtain the best solution from experts in a certain area or different solutions based on ideas from different people.  
CSE generally includes three actors: requesters (clients), workers, and platforms. Requesters are the ones that need the software development task to be done, workers participate in the actual development and are responsible for performing the work, and platforms provide an online marketplace where requesters and workers can meet.  
Although CSE is an emerging research area, it has been gaining increasing interest across the globe, and much research has already been published in this field [1, 2, 3]. However, there are a limited number of studies that outline and visualize the research trends in CSE, and these few studies simply evaluate and determine challenges for and potential improvements to CSE research [2, 4].  
This study aims to visualize and analyze CSE research using social network analysis (SNA) in order to understand research productivity, trends, performances, community structures, and relationships between various collaborative patterns and thus provide a more complete picture of the CSE research community. In particular, we aimed to study the social networks of co-authorship and citation of CSE research.  
SNA is the process of investigating the social structure of relationships in networks and its impact on the behavior, attitudes, and performance of the individual actors or groups affected by this social structure [5; 6]. SNA has been successfully used to better understand research in various fields, including medicine, sociology, information sciences, geography, and biology [7; 8; 9; 10]. SNA has also been used to analyze the publications of researchers in the software engineering field to better understand research collaborations and trends [11; 12].  
This study contributes to the improvement of research in the field of CSE and promotes collaborative research among researchers in the field. It helps practitioners and researchers understand the current state of CSE research in the world and inspires them to conduct further research.  
The study helps researchers to identify the most important authors in the field of CSE and the extent of their collaborations. This can encourage them to expand their network of contacts and collaborate with others from different institutions to improve their CSE research performance. In addition, it helps to identify the most important institutions and countries that have contributed significantly to CSE research and, therefore, improve the status of international collaboration. It also contributes to the field by presenting the most prominent and also the least interesting CSE research.  
This paper is organized as follows: Section 2 presents a literature review of studies on the use of SNA in research and in the software engineering area in particular. Our research data and method are presented in Section 3, and Section 4 explains our research results. A discussion about the results and conclusions is provided in Section 5.