Maintaining consistency across heterogeneous textual DSLs that describe the same system is a recurring challenge in software language engineering. We advocate a separation of concerns: (i) language-specific model-to-text synchronizations that relate the concrete syntax to its abstract representation as a model, and (ii) heterogeneous model-to-model synchronizations that capture the semantic overlap between languages. We extend the AnyText language workbench with a generic, grammar-based, incremental, bidirectional model-to-text synchronization by integrating its incremental parser and pretty-printer to propagate edits and support round-trip updates. Composing two such model-to-text synchronizations with an online-incremental, bidirectional model-to-model synchronization yields an end-to-end, modular, incremental, bidirectional text-to-text synchronization pipeline across DSLs. We demonstrate and evaluate the approach by synchronizing finite state machines and Petri nets, indicating a good performance of our approach also for large text documents. An open-source implementation is available under a BSD-3 license as part of NMF.

Many current language workbenches do not support incremental parsing or left recursion, lack support for resolving references and pretty-printing or make it difficult to implement the Language Server Protocol (LSP) based on the generated parser. In this paper, we present AnyText, a new language workbench that solves these challenges by implementing the LSP using a scannerless, incremental packrat parser with support for left recursion, based on a grammar description in an extended EBNF-like grammar similar to Xtext/Langium. Furthermore, we show how an EBNF extension with formatting instructions can be used to obtain a pretty-printer. We use a combination of an internal and an external DSL to simplify the process of creating a new language. We demonstrate our approach using two new DSLs and evaluate performance and the amount of manual code necessary to support different editor features.

Abstract To cope with the increased complexity of systems, models are used to capture what is considered the essence of a system. Such models are typically represented as a graph, which is queried to gain insight into the modelled system. Often, the results of these queries need to be adjusted according to updated requirements and are therefore a subject of maintenance activities. It is thus necessary to support writing model queries with adequate languages. However, in order to stay meaningful, the analysis results need to be refreshed as soon as the underlying models change. Therefore, a good execution speed is mandatory in order to cope with frequent model changes. In this paper, we propose a benchmark to assess model query technologies in the presence of model change sequences in the domain of social media. We present solutions to this benchmark in a variety of 11 different tools and compare them with respect to explicitness of incrementalization, asymptotic complexity and performance.

Despite the crucial importance of metamodeling for Model-Driven Engineering (MDE), there is still little discussion about the quality of metamodel design and its consequences in model-driven development processes. Presumably, the quality of metamodel design strongly affects the models and transformations that conform to these metamodels. However, so far surprisingly few work has been done to validate the characterization of metamodel quality. A proper characterization is essential to automate quality improvements for metamodels such as metamodel refactorings. In this paper, we present an empirical study to sharpen the understanding of the perception of metamodel quality. In the study, 24 participants created metamodels of two different domains and evaluated the metamodels in a peer review process according to an evaluation sheet. The results show that the perceived quality was mainly driven by the metamodels completeness, correctness and modularity while other quality attributes could be neglected.