# AI-Supported EUD for Data Visualization: An Exploratory Case Study
## 2 Background and Related Work
### End-User Development for Data Visualization  
EUD for data visualization has been, so far, an under-explored topic. In 2013, [PERSON] et al. [14] compared different research and commercial visualization development tools to investigate their suitability for end-user developers. The authors underlined how visualization development is traditionally carried out by professional programmers in collaboration with domain experts, but that such collaboration often leads to misunderstandings and longer development time. The examined tools offered graphical user interfaces with direct manipulation of visual objects, but most of them required professional programming skills or presented themselves as 'black boxes', preventing end users from creating visualizations different from predefined templates. Furthermore, it was observed that evaluation with users was usually ignored [14]. Thus, EUD is advocated as a solution to this problem; that is, providing end users (domain experts) with proper tools they can use to directly create or customize the desired visualizations for their data, as also already suggested in [15]. To this purpose, ref. [16] proposes uViz, which presents a very rich but complex graphical user interface, leading users to make errors during formula definition and data binding; indeed, participants in the user study claimed that users need some information technology skills and database knowledge to use it effectively. Starting from the analysis of users' practices, knowledge, and skills in a real organization, this paper aims to create a design probe that allows for the investigation of how to ensure a better fit between users and technology. Natural language-based interfaces allow one to overcome the difficulties encountered by end users with traditional query languages and manual plotting of visualizations. In their survey, [PERSON] et al. [17] investigated the evolution of methods and tools implement ing the translation of natural language into SQL (Text-to-SQL) and of natural language into visualization specifications (Text-to-Vis). Early approaches to text-to-SQL and Text-to-Vis were rule-based and template-based; then, neural networks and deep learning methods allowed a significant advancement in system performance; recently, pretrained language models (PLMs) and, above all, LLMs have offered new opportunities to develop easy-to-use interfaces for data query and visualization. As for the system architectures, end-to-end systems have emerged as the most suitable for end users; these systems process input questions (text or voice-based) and directly generate the desired output. Examples include Photon [18], VoiceQuerySystem [19], Sevi [20], and DeepTrack [21], but none of them are based on LLMs. To experiment with this novel technique and fill the gap related to the availability of end-to-end systems in the real world [17], this paper presents an LLM-based EUD environment, whose design was informed by the outcomes of the first phase of our case study research. The survey by [PERSON] et al. [22] analyzes papers proposing LLM-based systems for Text-to-SQL only, underlining that this has the potential to democratize access to data for those users who are not knowledgeable in SQL programming [23]. The LLM-based solution for Text-to-SQL has so far been more investigated than Text-to-Vis (e.g., [24; 25]) since it brings superior generation capabilities. However, most of the papers surveyed did not involve users in system design and evaluation, but benchmark datasets and automatic tests were used. Robustness in real-world scenarios is considered a challenge for future work [22]; in particular, it is important to fill the semantic gap between the user question and the database schema and cope with the relatively small size of real databases with respect to research-oriented benchmarks. Scoping our research in a real scenario with the participation of real users is meant to address this challenge.