Just like current software systems, conceptual models are characterised by increasing complexity and rate of change. Yet, these models only become useful if they can be continuously evaluated, validated and serialized. To achieve sufficiently low response times for large models, incremental analysis is required. Reference Attribute Grammars (RAGs) offer mechanisms to perform incremental analysis efficiently using dynamic dependency tracking. However, not all features used in conceptual modelling are directly available in RAGs. In particular, support for noncontainment model relations is only available through encodings. We present an approach called Relational RAGs to directly model uni- and bidirectional noncontainment relations in RAGs and provide efficient means for navigating and editing them. Furthermore, we discuss the efficient and inter-operable serialization and deserialization of such model instances. This approach is evaluated using a scalable benchmark for incremental model editing and the JastAdd RAG system. Our work demonstrates the suitability of RAGs for validating complex and continuously changing models of current software systems.

就像当前的软件系统一样，概念模型的特点是复杂性和变化率不断提高。但是，这些模型只有在可以对其进行连续评估，验证和序列化的情况下才有用。为了使大型模型的响应时间足够短，需要进行增量分析。参考属性语法（RAG）提供了使用动态依赖项跟踪有效执行增量分析的机制。但是，并非概念模型中使用的所有功能都可以在RAG中直接使用。特别是，仅通过编码即可获得对非约束模型关系的支持。我们提出一种称为关系RAG的方法直接在RAG中建模单向和双向非约束关系，并提供导航和编辑它们的有效方法。此外，我们讨论了此类模型实例的高效且可互操作的序列化和反序列化。使用用于增量模型编辑的可伸缩基准测试和JastAdd RAG系统对该方法进行评估。我们的工作证明了RAG适合验证当前软件系统的复杂且不断变化的模型。