Many testing and benchmarking scenarios in software and systems engineering depend on the systematic generation of graph models. For instance, tool qualification necessitated by safety standards would require a large set of consistent (well-formed or malformed) instance models specific to a domain. However, automatically generating consistent graph models which comply with a metamodel and satisfy all well-formedness constraints of industrial domains is a significant challenge. Existing solutions which map graph models into first-order logic specification to use back-end logic solvers (like Alloy or Z3) have severe scalability issues. In the paper, we propose a graph solver framework for the automated generation of consistent domain-specific instance models which operates directly over graphs by combining advanced techniques such as refinement of partial models, shape analysis, incremental graph query evaluation, and rule-based design space exploration to provide a more efficient guidance. Our initial performance evaluation carried out in four domains demonstrates that our approach is able to generate models which are 1-2 orders of magnitude larger (with 500 to 6000 objects!) compared to mapping-based approaches natively using Alloy.
Despite the wide range of existing tool support, constructing a design environment for a complex domain-specific language (DSL) is still a tedious task as the large number of derived features and well-formedness constraints complementing the domain metamodel necessitate special handling. Such derived features and constraints are frequently defined by declarative techniques (such graph patterns or OCL invariants).However, for complex domains, derived features and constraints can easily be formalized incorrectly resulting in inconsistent, incomplete or ambiguous DSL specifications. To detect such issues, we propose an automated mapping of EMF metamodels enriched with derived features and well-formedness constraints captured as graph queries in EMF-IncQuery or (a subset of) OCL invariants into an effectively propositional fragment of first-order logic which can be efficiently analyzed by back-end reasoners.On the conceptual level, the main added value of our encoding is (1) to transform graph patterns of the EMF-IncQuery framework into FOL and (2) to introduce approximations for complex language features (e.g. transitive closure or multiplicities) which are not expressible in FOL. On the practical level, we identify and address relevant challenges and scenarios for systematically validating DSL specifications. Our approach is supported by a tool and it will be illustrated on analyzing a DSL in the avionics domain. We also present initial performance experiments for the validation using Z3 and Alloy as back-end reasoners.
Automated model generation can be highly beneficial for various application scenarios including software tool certification, validation of cyber-physical systems or benchmarking graph databases to avoid tedious manual synthesis of models. In the paper, we present a long-term research challenge how to generate graph models specific to a domain which are consistent, diverse, scalable and realistic at the same time. We provide foundations for a class of model generators along a refinement relation which operates over partial models with 3-valued representation and ensures that subsequently derived partial models preserve the truth evaluation of well-formedness constraints in the domain. We formally prove completeness, i.e. any finite instance model of a domain can be generated by model generator transformations in finite steps and soundness, i.e. any instance model retrieved as a solution satisfies all well-formedness constraints. An experimental evaluation is carried out in the context of a statechart modeling tool to evaluate the trade-off between different characteristics of model generators.
Abstract. The generation of sample instance models of Domain-Specific Language (DSL) specifications has become an active research line due to its increasing industrial relevance for engineering complex modeling tools by using large metamodels and complex well-formedness constraints. However, the synthesis of large, well-formed and realistic models is still a major challenge. In this paper, we propose an iterative process for generating valid instance models by calling existing logic solvers as black-box components using various approximations of metamodels and constraints to improve overall scalability. (1) First, we apply enhanced metamodel pruning and partial instance models to reduce the complexity of model generation subtasks and the retrieved partial solutions initiated in each step. (2) Then we propose an (over-)approximation technique for wellformedness constraints in order to interpret and evaluate them on partial (pruned) metamodels. (3) Finally, we define a workflow that incrementally generates a sequence of instance models by refining and extending partial models in multiple steps, where each step is an independent call to the underlying solver (the Alloy Analyzer in our experiments).
Abstract. In the early stages of model driven development, models are frequently incomplete and partial. Partial models represent multiple possible concrete models, and thus, they are able to capture uncertainty and possible design decisions. When using models of a complex modeling language, several well-formedness constraints need to be continuously checked to highlight conceptual design flaws for the engineers in an early phase. While well-formedness constraints can be efficiently checked for (fully specified) concrete models, checking the same constraints over partial models is more challenging since, for instance, a currently valid constraint may be violated (or an invalid constraint may be respected) when refining a partial model into a concrete model. In this paper we propose a novel technique to evaluate well-formedness constraints on partial models in order to detect if (i) a concretization may potentially violate or (ii) any concretization will surely violate a well-formedness constraint to help engineers gradually to resolve uncertainty without violating well-formedness. For that purpose, we map the problem of constraint evaluation over partial models into a regular graph pattern matching problem over complete models by semantically equivalent rewrites of graph queries.
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