Towards Automatic Support of Software Model Evolution with Large Language~Models
This addresses the challenge of automating model evolution for software engineers, but it is incremental as it applies existing LLMs to a new domain.
The paper tackles the problem of supporting software model evolution by using large language models for model completion and discovering editing patterns, finding them to be a promising technology through controlled experiments with simulated repositories.
Modeling structure and behavior of software systems plays a crucial role, in various areas of software engineering. As with other software engineering artifacts, software models are subject to evolution. Supporting modelers in evolving models by model completion facilities and providing high-level edit operations such as frequently occurring editing patterns is still an open problem. Recently, large language models (i.e., generative neural networks) have garnered significant attention in various research areas, including software engineering. In this paper, we explore the potential of large language models in supporting the evolution of software models in software engineering. We propose an approach that utilizes large language models for model completion and discovering editing patterns in model histories of software systems. Through controlled experiments using simulated model repositories, we conduct an evaluation of the potential of large language models for these two tasks. We have found that large language models are indeed a promising technology for supporting software model evolution, and that it is worth investigating further in the area of software model evolution.