# Towards Automatic Support of Software Model Evolution with Large Language~Models

arXiv: https://arxiv.org/abs/2312.12404 · HTML: https://mlsanity.com/paper/2312.12404/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 75 references that are in the map.
- First posted on arXiv 19 Dec 2023.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [A Systematic Evaluation of Large Language Models of Code](https://mlsanity.com/paper/2202.13169/) (2022)
- [A Short Tutorial on The Weisfeiler-Lehman Test And Its Variants](https://mlsanity.com/paper/2201.07083/) (2022)
- [What Do They Capture? -- A Structural Analysis of Pre-Trained Language Models for Source Code](https://mlsanity.com/paper/2202.06840/) (2022)
- [Applying CodeBERT for Automated Program Repair of Java Simple Bugs](https://mlsanity.com/paper/2103.11626/) (2021)
- [Choose Your Programming Copilot: A Comparison of the Program Synthesis Performance of GitHub Copilot and Genetic Programming](https://mlsanity.com/paper/2111.07875/) (2021)
- [An Empirical Study on the Usage of Transformer Models for Code Completion](https://mlsanity.com/paper/2108.01585/) (2021)
- [Interpreting Language Models Through Knowledge Graph Extraction](https://mlsanity.com/paper/2111.08546/) (2021)
- [Recommending Metamodel Concepts during Modeling Activities with Pre-Trained Language Models](https://mlsanity.com/paper/2104.01642/) (2021)
- [SLGPT: Using Transfer Learning to Directly Generate Simulink Model Files and Find Bugs in the Simulink Toolchain](https://mlsanity.com/paper/2105.07465/) (2021)
- [Learning Domain-Specific Edit Operations from Model Repositories with Frequent Subgraph Mining](https://mlsanity.com/paper/2108.01001/) (2021)
- [Systematically Deriving Domain-Specific Transformation Languages](https://mlsanity.com/paper/1511.05366/) (2015)
- [Can Language Models Capture Graph Semantics? From Graphs to Language Model and Vice-Versa](https://mlsanity.com/paper/2206.09259/) (2022)

## Abstract (arXiv)

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.