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

• URL: http://arxiv.org/abs/2606.24721v1
• Date: Tue, 23 Jun 2026 15:47:14 GMT
• Title: Two-Level vs. Multi-Level Modelling: An Empirical Study of Cascading Maintenance Burden
• Abstract summary: Multi-level modelling (MLM) unifies artefacts and is claimed to reduce co-evolution burden.<n>We present a pre-registered, mutation-based empirical comparison of co-evolution behaviour in both paradigms.<n>This design provides the first empirical framework for assessing whether paradigm-level structural choices affect cascading maintenance burden.
• Score: 2.992535912564069
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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