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In recent decades, the impact of data science and communication technology in several domains– particularly the healthcare domain– has been profound to an unprecedented level (Chen et al., 2016). Among the several data sciences and communication innovations, software plays a vital role that requires proper analysis and design process (Carroll & Richardson, 2016). A good example central to data science is how persistent illnesses and chronic diseases in healthcare are managed. Notably, data-driven calculations can be made with technology's support to improve healthcare delivery (Chen et al., 2016) and quality of care for patients (Gambo and Soriyan, 2017). This technology assists with monitoring, diagnosis, and smart systems (Jovanov et al., 2005; Bossen et al., 2015; Chen et al., 2016; Pramanik et al., 2017; Gambo et al., 2020). However, these systems are critical because they involve patients living on a large and continued scale. As a result, they are susceptible to mistakes even more if they come from the requirements engineering (RE) process (Bjørner, 2009; Katina et al., 2014; Liu et al., 2016).
Therefore, engineering such a smart software to automatically monitor, diagnose, and predict the blood glucose (BG) levels of patients with T1DM necessitates an appropriate method of RE. In the literature, RE is a recognized phase in the software project lifecycle for determining the quality and success of software-based solutions (Arif et al., 2010; Alshazly et al., 2014; Gambo et al., 2014; Teixeira et al., 2014). Unfortunately, failure in most software-based solutions installed in embedded systems like the Implantable Medical Devices (IMDs) and other hardware-related systems used to control, monitor, and predict continuous BG is due to a lack of a good RE process (Zave, 1982; Rahman, 2014). Deficiency in most of these systems could lead to death in the healthcare domain and other environmental systems connected to life. It is noteworthy to say that when the patient's need is not adequately met and the features of the IMDs are not correctly captured, analyzed, and designed, the system's functionality will not be trusted and reliable (Fricker et al., 2015).
Moreover, the present emergency hospitals use various smart devices for different medical applications, including the patient's monitoring, analysis, treatment, and therapeutic care, such as surgical robots used for operations. Still, the opportunities for utilizing these smart devices in the healthcare field include introducing software components (Sanislav et al., 2012). The software assumes a significant role in therapeutic devices (Pashkov et al., 2016), and software engineering serves as a critical competency for building these kinds of medical device systems (Nessi, 2014). Crucial in smart engineering systems that engender healthcare delivery quality is an appropriate software engineering method. Besides, requirements development is one of the most challenging components of engineering software-intensive systems of high quality (Chung et al., 2012). In most engineering techniques, a great deal of effort is on the functional and non-functional targets of a system. For instance, software specialists often design a product from a set of preferred functional and non-functional objectives primarily based on their perception of the system, resulting in an end product that does not meet supposed end-users needs (Kissoon et al., 2019).