Oszkár Semeráth

Formal validation of domain-specific languages with derived features and well-formedness constraints

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.

Iterative and Incremental Model Generation byźLogic Solvers

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 well-formedness 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.

Graph Constraint Evaluation over Partial Models by Constraint Rewriting

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.

Validation of Derived Features and Well-Formedness Constraints in DSLs

Despite the wide range of existing generative 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. Incremental model queries as provided by the EMF-IncQuery framework can i uniformly specify derived features and well-formedness constraints and ii automatically refresh their result set upon model changes. However, for complex domains, derived features and constraints can be formalized incorrectly resulting in incomplete, ambiguous or inconsistent 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 into an effectively propositional fragment of first-order logic which can be efficiently analyzed by the Z3 SMT-solver. Moreover, overapproximations are proposed for complex query features like transitive closure and recursive calls. Our approach will be illustrated on analyzing a DSL being developed for the avionics domain.

Towards the Automated Generation of Consistent, Diverse, Scalable and Realistic Graph Models

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.

Incremental backward change propagation of view models by logic solvers

View models are key concepts of domain-specific modeling to provide task-specific focus (e.g., power or communication architecture of a system) to the designers by highlighting only the relevant aspects of the system. View models can be specified by unidirectional forward transformations (frequently captured by graph queries), and automatically maintained upon changes of the underlying source model using incremental transformation techniques. However, tracing back complex changes from one or more abstract view to the underlying source model is a challenging task, which, in general, requires the simultaneous analysis of transformation specifications and well-formedness constraints to create valid changes in the source model. In this paper we introduce a novel delta-based backward transformation technique using SAT solvers to synthetize valid and consistent change candidates in the source model, where only forward transformation rules are specified for the view models.

Iterative Generation of Diverse Models for Testing Specifications of DSL Tools

The validation of modeling tools of custom domain-specific languages (DSLs) frequently relies upon an automatically generated set of models as a test suite. While many software testing approaches recommend that this test suite should be diverse, model diversity has not been studied systematically for graph models. In the paper, we propose diversity metrics for models by exploiting neighborhood shapes as abstraction. Furthermore, we propose an iterative model generation technique to synthesize a diverse set of models where each model is taken from a different equivalence class as defined by neighborhood shapes. We evaluate our diversity metrics in the context of mutation testing for an industrial DSL and compare our model generation technique with the popular model generator Alloy.

A graph solver for the automated generation of consistent domain-specific models

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.

Evaluating Well-Formedness Constraints on Incomplete Models

In modern modeling tools used for model-driven development, the validation of several well-formedness constraints is continuously been carried out by exploiting advanced graph query engines to highlight conceptual design flaws. However, while models are still under development, they are frequently partial and incomplete. Validating constraints on incomplete, partial models may identify a large number of irrelevant problems. By switching off the validation of these constraints, one may fail to reveal problematic cases which are difficult to correct when the model becomes sufficiently detailed. Here, we propose a novel validation technique for evaluating well-formedness constraints on incomplete, partial models with may and must semantics, e.g. a constraint without a valid match is satisfiable if there is a completion of the partial model that may satisfy it. To this end, we map the problem of constraint evaluation over partial models into regular graph pattern matching over complete models by semantically equivalent rewrites of graph queries.

Incremental View Model Synchronization Using Partial Models

View models are abstractions of a set of source models derived by unidirectional model transformations. In this paper, we propose a view model transformation approach which provides a fully compositional transformation language built on an existing graph query language to declaratively compose source and target patterns into transformation rules. Moreover, we provide a reactive, incremental, validating and inconsistency-tolerant transformation engine that reacts to changes of the source model and maintains an intermediate partial model by merging the results of composable view transformations followed by incremental updates of the target view. An initial scalability evaluation of an open source prototype tool built on top of an open source model transformation tool is carried out in the context of the open Train Benchmark framework.