Behavioral models are important assets that must be thoroughly verified early in the design process. This can be achieved with manually-written test cases that embed carefully hand-picked domain-specific input data. However, such test cases may not always reach the desired level of quality, such as high coverage or being able to localize faults efficiently. Test amplification is an interesting emergent approach to improve a test suite by automatically generating new test cases out of existing manually-written ones. Yet, while ad-hoc test amplification solutions have been proposed for a few programming languages, no solution currently exists for amplifying the test cases of behavioral models.

To test a system efficiently, we need to know how good are the defined test cases and to localize detected faults in the system. Measuring test coverage can address both concerns as it is a popular metric for test quality evaluation and, at the same time, is the foundation of advanced fault localization techniques. However, for Domain-Specific Languages (DSLs), coverage metrics and associated tools are usually manually defined for each DSL representing costly, error-prone, and non-reusable work.

Abstract Executable domain-specific languages (DSLs) enable the execution of behavioral models. While an execution is mostly driven by the model content (e.g., control structures), many use cases require interacting with the running model, such as simulating scenarios in an automated or interactive way, or coupling the model with other models of the system or environment. The management of these interactions is usually hardcoded into the semantics of the DSL, which prevents its reuse for other DSLs and the provision of generic interaction-centric tools (e.g., event injector). In this paper, we propose a metalanguage for complementing the definition of executable DSLs with explicit behavioral interfaces to enable external tools to interact with executed models in a unified way. We implemented the proposed metalanguage in the GEMOC Studio and show how behavioral interfaces enable the realization of tools that are generic and thus usable for different executable DSLs.

Executable Domain-Specific Languages (DSLs) are commonly defined with either operational semantics (i.e., interpretation) or translational semantics (i.e., compilation). An interpreted DSL relies on domain concepts to specify the possible execution states and steps, which enables the observation and control of executions using the very same domain concepts. In contrast, a compiled DSL relies on a transformation to an arbitrarily different target language. This creates a conceptual gap, where the execution can only be observed and controlled through target domain concepts, to the detriment of experts or tools that only understand the source domain. To address this problem, we propose a language engineering architecture for compiled DSLs that enables the observation and control of executions using source domain concepts. The architecture requires the definition of the source domain execution steps and states, along with a feedback manager that translates steps and states of the target domain back to the source domain. We evaluate the architecture with two different compiled DSLs, and show that it does enable domain-level observation and control while increasing execution time by 2× in the worst observed case.

Executable Domain-Specific Modeling Languages (xDSMLs) enable the application of early dynamic verification and validation (V&V) techniques for behavioral models. At the core of such techniques, execution traces are used to represent the evolution of models during their execution. In order to construct execution traces for any xDSML, generic trace metamodels can be used. Yet, regarding trace manipulations, generic trace metamodels lack efficiency in time because of their sequential structure, efficiency in memory because they capture superfluous data, and usability because of their conceptual gap with the considered xDSML. Our contribution is a novel generative approach that defines a multidimensional and domain-specific trace metamodel enabling the construction and manipulation of execution traces for models conforming to a given xDSML. Efficiency in time is improved by providing a variety of navigation paths within traces, while usability and memory are improved by narrowing the scope of trace metamodels to fit the considered xDSML. We evaluated our approach by generating a trace metamodel for fUML and using it for semantic differencing, which is an important V&V technique in the realm of model evolution. Results show a significant performance improvement and simplification of the semantic differencing rules as compared to the usage of a generic trace metamodel.

Omniscient debugging is a promising technique that relies on execution traces to enable free traversal of the states reached by a system during an execution. While some General-Purpose Languages (GPLs) already have support for omniscient debugging, developing such a complex tool for any executable Domain-Specific Modeling Language (xDSML) remains a challenging and error prone task. A solution to this problem is to define a generic omniscient debugger for all xDSMLs. However, generically supporting any xDSML both compromises the efficiency and the usability of such an approach. Our contribution relies on a partly generic omniscient debugger supported by generated domain-specific trace management facilities. Being domain-specific, these facilities are tuned to the considered xDSML for better efficiency. Usability is strengthened by providing multidimensional omniscient debugging. Results show that our approach is on average 3.0 times more efficient in memory and 5.03 more efficient in time when compared to a generic solution that copies the model at each step.