arXiv subjects

Aaron Conrardy

Publications and source records attributed to Aaron Conrardy.

8 recordsLinked to original sources

arXiv2026

Low-code and no-code with BESSER to create and deploy smart web applications

The increasing demand for web applications containing AI-agents, seen as smart web applications, has prompted the need for new techniques to facilitate their creation. Low-code has risen as an approach that reduces the amount of handwritten code by focusing on the abstraction of components in the form of models combined with automated generators to produce applications. Existing low-code platforms are commercial, leading to drawbacks such as the risk of vendor lock-in, limited extensibility, and more. We present the open-source BESSER low-code framework, which allows users to design, generate and deploy their application via a freely accessible web-based editor, while guaranteeing transparency and extensibility.

cs.SE↗

arXiv2026

A Low-Code Approach for the Automatic Personalization of Conversational Agents

The rise of Large Language Models (LLMs) has increased the demand for Conversational Agents (CAs) capable of understanding human conversations as part of web applications. While traditional CAs consist of deterministic states, LLMs enhance their capabilities to handle open conversations, handling arbitrary requests. Numerous tools exist that allow non-technical users to create such CAs. Yet, the creation of personalized CAs able to adapt to the profile of end-users to offer an optimal user experience remains in the hands of experienced developers implementing ad-hoc personalizations. In this work, we propose a pipeline that follows a low-code/no-code approach to facilitate the modeling and generation of personalized CAs. A pilot user study was performed to get preliminary results on perceived usability and usefulness and the full pipeline has been implemented on top of an open-source low-code platform.

cs.SE↗

arXiv2025

Low-code to fight climate change: the Climaborough project

The EU-funded Climaborough project supports European cities to achieve carbon neutrality by 2030. Eleven cities in nine countries will deploy in real conditions products and services fostering climate transition in their local environment. The Climaborough City Platform is being developed to monitor the cities' overall progress towards their climate goals by aggregating historic and real-time data and displaying the results in user-friendly dashboards that will be used by non-technical experts to evaluate the effectiveness of local experimental initiatives, identify those that yield significant impact, and assess the potential consequences of scaling them up to a broader level. In this paper, we explain how we have put in place a low-code/no-code strategy in Climaborough in response to the project's aim to quickly deploy climate dashboards. A low-code strategy is used to accelerate the development of the dashboards. The dashboards embed a no-code philosophy that enables all types of citizen profiles to configure and adapt the dashboard to their specific needs.

cs.SE↗

arXiv2025

Towards a unified user modeling language for engineering human centered AI systems

In today's digital society, personalization has become a crucial aspect of software applications, significantly impacting user experience and engagement. A new wave of intelligent user interfaces, such as AI-based conversational agents, has the potential to enable such personalization beyond what other types of interfaces could offer in the past. Personalization requires the ability to specify a complete user profile, covering as many dimensions as possible, such as potential accessibility constraints, interaction preferences, and even hobbies. Yet, existing solutions for user modeling mostly focus on individual aspects at a very coarse level, severely limiting the potential adaptations for personalization. In this sense, this paper presents a unified user modeling language, aimed to combine previous approaches, both from the modeling community and other user-centric fields, in a single proposal. This language has been implemented on top of the open source BESSER low-code platform. Additionally, a proof of concept leveraging user profiles modeled with our language to automatically adapt a conversational agent has also been developed.

cs.SE↗

arXiv2024

User Modeling in Model-Driven Engineering: A Systematic Literature Review

In software applications, user models can be used to specify the profile of the typical users of the application, including personality traits, preferences, skills, etc. In theory, this would enable an adaptive application behavior that could lead to a better user experience. Nevertheless, user models do not seem to be part of standard modeling languages nor common in current model-driven engineering (MDE) approaches. In this paper, we conduct a systematic literature review to analyze existing proposals for user modeling in MDE and identify their limitations. The results showcase that there is a lack of a unified and complete user modeling perspective. Instead, we observe a lot of fragmented and partial proposals considering only simple user dimensions and with lack of proper tool support. This limits the implementation of richer user interfaces able to better support the user-specific needs. Therefore, we hope this analysis triggers a discussion on the importance of user models and their inclusion in MDE pipelines. Especially in a context where, thanks to the rise of AI techniques, personalization, based on a rich number of user dimensions, is becoming more and more of a possibility.

cs.SE↗

arXiv2024

Towards the interoperability of low-code platforms

With the promise of accelerating software development, low-code platforms (LCPs) are becoming popular across various industries. Nevertheless, there are still barriers hindering their adoption. Among them, vendor lock-in is a major concern, especially considering the lack of interoperability between these platforms. Typically, after modeling an application in one LCP, migrating to another requires starting from scratch remodeling everything (the data model, the graphical user interface, workflows, etc.), in the new platform. To overcome this situation, this work proposes an approach to improve the interoperability of LCPs by (semi)automatically migrating models specified in one platform to another one. The concrete migration path depends on the capabilities of the source and target tools. We first analyze popular LCPs, characterize their import and export alternatives and define transformations between those data formats when available. This is then complemented with an LLM-based solution, where image recognition features of large language models are employed to migrate models based on a simple image export of the model at hand. The full pipelines are implemented on top of the BESSER modeling framework that acts as a pivot representation between the tools.

cs.SE↗

arXiv2024

Building BESSER: an open-source low-code platform

Low-code platforms (latest reincarnation of the long tradition of model-driven engineering approaches) have the potential of saving us countless hours of repetitive boilerplate coding tasks. However, as software systems grow in complexity, low-code platforms need to adapt as well. Notably, nowadays this implies adapting to the modeling and generation of smart software. At the same time, if we want to broaden the userbase of this type of tools, we should also be able to provide more open source alternatives that help potential users avoid vendor lock-ins and give them the freedom to explore low-code development approaches (even adapting the tool to better fit their needs). To fulfil these needs, we are building BESSER, an open source low-code platform for developing (smart) software. BESSER offers various forms (i.e., notations) for system and domain specification (e.g. UML for technical users and chatbots for business users) together with a number of generators. Both types of components can be extended and are open to contributions from the community.

cs.SE↗

arXiv2024

From Image to UML: First Results of Image Based UML Diagram Generation Using LLMs

In software engineering processes, systems are first specified using a modeling language such as UML. These initial designs are often collaboratively created, many times in meetings where different domain experts use whiteboards, paper or other types of quick supports to create drawings and blueprints that then will need to be formalized. These proper, machine-readable, models are key to ensure models can be part of automated processes (e.g. input of a low-code generation pipeline, a model-based testing system, ...). But going from hand-drawn diagrams to actual models is a time-consuming process that sometimes ends up with such drawings just added as informal images to the software documentation, reducing their value a lot. To avoid this tedious task, we explore the usage of Large Language Models (LLM) to generate the formal representation of (UML) models from a given drawing. More specifically, we have evaluated the capabilities of different LLMs to convert images of UML class diagrams into the actual models represented in the images. While the results are good enough to use such an approach as part of a model-driven engineering pipeline we also highlight some of their current limitations and the need to keep the human in the loop to overcome those limitations.

cs.SE↗