Andreas Biesdorf | Searcharxiv

arXiv2026

Model-Driven Legacy System Modernization at Scale

This experience report presents a model-driven approach to legacy system modernization that inserts an enriched, technology-agnostic intermediate model between the legacy codebase and the modern target platform, and reports on its application and evaluation. The four-stage process of analysis, enrichment, synthesis, and transition systematically extracts, abstracts, and transforms system artifacts. We apply our approach to a large industrial application built on legacy versions of the .NET Framework and ASP.NET MVC and show that core user interface components and page structures can be migrated semi-automatically to a modern web stack while preserving functional behavior and essential non-functional qualities. By consolidating architectural knowledge into explicit model representations, the resulting codebase exhibits higher maintainability and extensibility, thereby improving developer experience. Although automation is effective for standard patterns, migration of bespoke layout composites remains challenging and requires targeted manual adaptation. Our contributions are: (i) an end-to-end model-driven process, (ii) an enriched intermediate model that captures structure, dependencies, and semantic metadata, (iii) transformation rules that preserve functional behavior and essential non-functional qualities, and (iv) application and evaluation of the approach in an industrial setting. Overall, model-based abstractions reduce risk and effort while supporting scalable, traceable modernization of legacy applications. Our approach generalizes to comparable modernization contexts and promotes reuse of migration patterns.

cs.SE↗

arXiv2021

Learning Domain-Specific Edit Operations from Model Repositories with Frequent Subgraph Mining

Model transformations play a fundamental role in model-driven software development. They can be used to solve or support central tasks, such as creating models, handling model co-evolution, and model merging. In the past, various (semi-)automatic approaches have been proposed to derive model transformations from meta-models or from examples. These approaches require time-consuming handcrafting or recording of concrete examples, or they are unable to derive complex transformations. We propose a novel unsupervised approach, called Ockham, which is able to learn edit operations from model histories in model repositories. Ockham is based on the idea that meaningful edit operations will be the ones that compress the model differences. We evaluate our approach in two controlled experiments and one real-world case study of a large-scale industrial model-driven architecture project in the railway domain. We find that our approach is able to discover frequent edit operations that have actually been applied. Furthermore, Ockham is able to extract edit operations in an industrial setting that are meaningful to practitioners.

cs.SE↗