Class Model Normalization - Outperforming Formal Concept Analysis Approaches with AOC-posets

Designing or reengineering class models in the domain of programming or modeling involves capturing technical and domain concepts, finding the right abstractions and avoiding duplications. Making this last task in a systematic way corresponds to a kind of model normalization. Several approaches have been proposed, that all converge towards the use of Formal Concept Analysis (FCA). An extension of FCA to linked data, Relational Concept Analysis (RCA) helped to mine better reusable abstractions. But RCA relies on iteratively building concept lattices, which may cause a combinatorial explosion in the number of the built artifacts. In this paper, we investigate the use of an alternative RCA process, relying on a specific sub-order of the concept lattice (AOC-poset) which preserves the most relevant part of the normal form. We measure, on case studies from Java models extracted from Java code and from UML models, the practical reduction that AOC-posets bring to the normal form of the class model.