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Conference Papers Year : 2023

On Programming Variability with Large Language Model-based Assistant


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.
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Dates and versions

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



  • HAL Id : hal-04153310 , version 1


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⟩
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