## 1 Introduction  
The development of modern software applications is a complex process requiring a multidisciplinary team. The diversity of backgrounds among the members of such teams causes communication barriers and difficulties in sharing a common vision of the systems to be developed [1]. Requirements engineering (RE) practices help mitigate these barriers' impacts and increase software development teams' productivity [2]. RE has proven to be a crucial collaboration facilitator [3], as business requirements specifications are defined in natural languages (or controlled natural languages) that are easier to understand than programming languages. Low-code development platforms (or just \"low-code platforms\" for brevity) started to grow as the need to create applications that quickly adapt to urgent market demands increased [4, 5, 6]. Low-code platforms intend to reduce the development and maintenance effort required to deliver and operate some application classes and enable digital-savvy citizen developers (who lack or have limited programming experience) to contribute directly to the software development process [7]. Considering a spectrum where controlled natural languages and programming languages are opposite ends, low-code platform languages stand somewhere in the middle; most of them allow for specifying business requirements more comprehensively than programming languages do. However, they are not as abstract as platform-independent specification languages. Low-code platforms such as Quidgest Genio [8], OutSystems [9], or Mendix [10] can be particularly beneficial at the prototyping stages of the development process, as experienced users of these tools can create simple yet fully functional systems in a matter of days or weeks. Currently, most large cloud providers offer low-code platforms within their cloud-based solutions. For instance, Microsoft released its Power Apps framework [11] in November 2016; Google acquired the provider AppSheet [12] and made it its primary low-code solution in January 2020; and Amazon released Honeycode [13], a low-code platform for web and mobile application development, in June 2020. On the other hand, model-driven engineering (MDE) is an approach that considers models not just as documentation artefacts but also as first-class citizens, where models might be used throughout all engineering disciplines and in any application domain [14]. As MDE has similar goals to low-code development, there is an open debate on how low-code development differs from MDE and to what extent work carried out in the field of MDE can be directly transferable to low-code platforms [15]. For instance, [PERSON] et al. discuss the commonalities and differences between both approaches, concluding that not all model-driven techniques aim at reducing the amount of code needed to implement software solutions, and not all low-code approaches are model-driven [7]. In the scope of this debate, our research intends to show and discuss the combination of a platform-independent language for the specification of software applications, ITLingo ASL [3], with a concrete low-code software development platform, Quidgest Genio [8]. First, we discuss how ASL compares to the Genio language to evaluate the possibility of automatically developing mechanisms to transform ASL specifications into Genio projects. Then, we take the findings of this analysis and design the proposed transformation, represented by the ASL2 LC task in Figure 1. It is out of the scope of this paper to discuss the second transformation illustrated in Figure 1 as the LC2 Code (code generation) task. Indeed, this transformation has been extensively implemented and evaluated in hundreds of applications of the Genio framework [16]. ITLingo is a research initiative that has proposed new languages, tools, and techniques to support users in improving their practices mainly related to project management, requirements engineering, and system design.