Mathieu Acher
Variability in Practice Track
 | Haus der Universität, Schlösslistrasse 5, 3008 Bern, Switzerland |
 | 9 February 2024, 10:50 CET |
 | Mathieu Acher |
 | Sandra Greiner |
 | https://dl.acm.org/doi/10.1145/3634713.3634732, https://www.slideshare.net/slideshows/a-demonstration-of-enduser-code-customization-using-generative-ai/266239123 |
Producing a variant of code is highly challenging, particularly for individuals unfamiliar with programming. This demonstration introduces a novel use of generative AI to aid end-users in customizing code. We first describe how generative AI can be used to customize code through prompts and instructions, and further demonstrate its potential in building end-user tools for configuring code. We showcase how to transform an undocumented, technical, low-level TikZ into a user-friendly, configurable, Web-based customization tool written in Python, HTML, CSS, and JavaScript and itself configurable. We discuss how generative AI can support this transformation process and traditional variability engineering tasks, such as identification and implementation of features, synthesis of a template code generator, and development of end-user configurators. We believe it is a first step towards democratizing variability programming, opening a path for end-users to adapt code to their needs.