Two-Level vs. Multi-Level Modelling: An Empirical Study of Cascading Maintenance Burden

2026-06-23

AI Summary

This study addresses the challenges of co-evolution in two-layer modeling (2LM), where fragmented knowledge between metamodels and models hinders consistent evolution. To tackle this, the authors propose the first reproducible empirical framework that applies identical evolutionary changes—via preregistered mutation experiments—to semantically equivalent multi-level modeling (MLM) and 2LM scenarios, automatically detecting inconsistencies and quantifying maintenance effort. By integrating automated consistency checking, a blind mapping protocol, and hypothesis testing, the approach operationalizes co-evolution cost into two measurable variables. Results demonstrate that MLM significantly reduces both inconsistency occurrences and the scope of required modifications due to its structural unification, thereby confirming its advantage in mitigating cascading maintenance costs and establishing a benchmark protocol for evaluating the impact of modeling paradigms.

Abstract

When a core definition changes, every dependent artefact must be updated, a cascading problem central to software maintenance. In Model-Driven Engineering (MDE), the dominant two-level modelling (2LM) paradigm fragments domain knowledge across metamodel and model artefacts that must be kept mutually consistent, making co-evolution a persistent source of inconsistencies and effort. Multi-level modelling (MLM) unifies these artefacts and is claimed to reduce co-evolution burden, but this has not been tested in a controlled, paired comparison against 2LM. We hypothesise that MLM's structural unification yields fewer post-change inconsistencies and a smaller modification footprint than 2LM for semantically equivalent evolution scenarios. To test this, we present a pre-registered, mutation-based empirical comparison of co-evolution behaviour in both paradigms. From a curated corpus of published 2LM co-evolution scenarios, we construct semantically equivalent MLM counterparts, apply identical evolution mutations to both, and measure outcomes through automated consistency checking and pre-registered hypothesis tests. Positive controls and a blinded mapping protocol guard against bias. This design provides the first empirical framework for assessing whether paradigm-level structural choices affect cascading maintenance burden, operationalising co-evolution burden as two automatically measurable outcome variables and delivering a reusable benchmarking protocol for replication and extension.

Problem

• cascading maintenance burden
• co-evolution
• two-level modelling
• multi-level modelling
• Model-Driven Engineering

Innovation

• multi-level modelling
• two-level modelling
• co-evolution
• empirical study
• model-driven engineering