On Programming Variability with Large Language Model-based Assistant - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2023

On Programming Variability with Large Language Model-based Assistant

Abstract

Programming variability is central to the design and implementation of software systems that can adapt to a variety of contexts and requirements, providing increased flexibility and customization. Managing the complexity that arises from having multiple features, variations, and possible configurations is known to be highly challenging for software developers. In this paper, we explore how large language model (LLM)-based assistants can support the programming of variability. We report on new approaches made possible with LLM-based assistants, like: features and variations can be implemented as prompts; augmentation of variability out of LLM-based domain knowledge; seamless implementation of variability in different kinds of artefacts, programming languages, and frameworks, at different binding times (compile-time or run-time). We are sharing our data (prompts, sessions, generated code, etc.) to support the assessment of the effectiveness and robustness of LLMs for variability-related tasks.
Fichier principal
Vignette du fichier
ProgrammingVariabilityGPT-SPLC23.pdf (563.77 Ko) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-04153310 , version 1 (06-07-2023)

Licence

Identifiers

  • HAL Id : hal-04153310 , version 1

Cite

Mathieu Acher, José Galindo Duarte, Jean-Marc Jézéquel. On Programming Variability with Large Language Model-based Assistant. SPLC 2023 - 27th ACM International Systems and Software Product Lines Conference, ACM, Aug 2023, Tokyo, Japan. pp.1-7. ⟨hal-04153310⟩
189 View
428 Download

Share

Gmail Mastodon Facebook X LinkedIn More