Low-Code Platforms (LCPs) and AI assistants accelerate and democratise application development by generating artefacts through visual tools, drag-and-drop components, prebuilt logic, and large language models (LLMs) for professional and citizen developers. While prior work has examined these approaches in isolation, the comparative use of these approaches for developing applications remains insufficiently understood. This paper presents a case study, in which multiple teams developed a web application based on the same requirements over 13 weeks using LCPs, LCPs with AI integration, or AI-assisted development. The study combines report and repository analysis with longitudinal survey data to capture technology usage, development processes, and developer perceptions. This primarily qualitative study aims to identify new insights into the potential and limitations of web development with LCPs and AI assistants. Our results suggest that LCPs with integrated AI enhance development efficiency and predictability compared to traditional LCPs and purely AI-assisted development.
L. Hofmann, Philipp Wieber, Gabriele Taentzer· Proceedings of the ACM/IEEE...· 0 citations
High-quality data is essential for reliable analysis, decision-making, and research across domains. This is especially relevant in areas such as cultural heritage, where data is collected and curated manually, making it prone to quality issues like inconsistencies. To improve data quality, the data must be analyzed regularly using systematic quality analyses. Quality analyses validate the conformance of data to domain-specific expectations. These expectations are best understood by domain experts, who can express them using natural language. However, they rarely possess the technical expertise to formalize these expectations into executable quality analyses. Consequently, this process requires domain experts and data engineers, making it time-consuming and technically demanding. The required technical expertise and the resulting dependencies pose a significant challenge. To address this challenge, we present a pipeline for formalizing and operationalizing data quality constraints. We support this pipeline using QPM, a metamodel for defining templates for reusable quality analyses. The web application Constrainify enables tailoring templates to specific conceptual requirements and translating them into executable quality analyses via a tool-chain based on model-driven engineering subpipelines. The result is a set of reusable, repeatable, and domain-specific quality analyses.
Arno Kesper, L. Hofmann, Markus Matoni et al.· 0 citations
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