Publications
Type of Publication: Article in Collected Edition
Property-Precedence Analysis for Model Deepening
- Author(s):
- Maier, Pierre
- Title of Anthology:
- ACM/IEEE 29th International Conference on Model Driven Engineering Languages and Systems (MODELS Companion 2026), October 04--09, 2026, Málaga, Spain
- Publication Date:
- 2026
- Digital Object Identifier (DOI):
- doi:10.1145/3837062.3838704
- Fulltext:
- PropertyPrecedenceAnalysis____MULTI26__Camera_ready_.pdf
- Citation:
- Download BibTeX
Abstract
Despite widely accepted prospects, multi-level modeling remains sparsely adopted in industry. Model deepening --, i.e., the re-engineering of flat representations into multi-level models -- has the potential to facilitate the industrial adoption of multi-level modeling by transforming models and representations of information systems an organization already possesses. Prior research has focused primarily on transformation operations; comparatively little attention has been devoted to the preceding analysis problem of identifying beneficial model-deepening transformations. Existing approaches typically rely on type-level heuristics such as naming patterns, aggregation relationships, or association multiplicities. Since these heuristics suggest multi-level hierarchies based on modeling choices and conventions rather than the represented instance population, they cannot reliably distinguish genuine classification levels from incidental modeling choices.
This paper proposes property-precedence analysis, an instance-driven approach to conduct model-deepening analysis. Building upon Bunge's ontological notion of property precedence, the proposed approach uncovers possible multi-level hierarchies by analyzing the distribution of property values across model instances rather than relying on type-level heuristics. The analysis is implemented in the prototype ModelDeepener for the MLM language FMMLx and demonstrated using two example models involving associations and attributes. Several challenges in property-precedence analysis for model deepening remain, such as handling conflicting precedence relations and naming newly suggested classes. Further research should investigate to what degree the identified challenges may be counteracted and how property-precedence analysis may be complemented with type-level heuristics for model deepening.