Embedding Component Behavior DSLs into the MontiArcAutomaton ADL

Abstract

Component & connector architecture description languages often need to capture application-speci c or company-speci c requirements. Therefore, it is a crucial prerequisite for their successful application to adapt the ADLs by customizing the languages themselves. Pervasive modeling with tailored ADLs can bene t from integration of DSLs to model-speci c forms of component behavior. This requires expertise of the underlying language integration mechanisms. Current research in integrating heterogeneous component behavior DSLs into an ADL focuses on integration of speci c kinds of DSLs or is restricted to syntactic integration. However, language integrators can be liberated from requiring in-depth language integration expertise using appropriate abstractions. To this e ect, we present a compact DSL for the integration of behavior DSLs into a component & connector ADL that guides and facilitates this form of language integration. Modeling the embedding of behavior DSLs into ADLs facilitates their composition and ultimately the pervasive modeling of complex architectures.

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For a good acceptance of component & connector (C&C) architecture description languages (ADLs) [ 17 ], adapting to application-speci c or company-speci c requirements is a crucial prerequisite [ 16 ]. Where domain experts contribute component behavior, adaptation might include integrating appropriate component behavior DSLs into the ADL of choice [ 8,18 ]. Model-driven development and meta modeling can support such adaptation [ 15 ], but require expertise of the underlying language workbenches [ 6 ], particularly their de nition and integration mechanisms [ 3 ].

Language workbenches usually provide means to de ne a subset of abstract syntax, concrete syntax, static semantics, and dynamic semantics for DSLs as well as generic integration mechanisms, such as importing, inheritance, or embedding, on top of these de nitions (such as Eco [ 5 ], Kermeta [ 12 ], or Spoofax [ 25 ]). While providing powerful means to compose new DSLs from existing ones, these integration mechanisms are usually unaware of their operation context and hence impose that language customization requires extensive language workbench implementation knowledge. However, where the context of DSL integration is restricted, such as inheritance from a xed parent DSL or embedding a DSL into a well-de ned extension point of an ADL, the degrees of integration freedom can be greatly reduced to facilitate language integration.

We present a DSL for the adaptation of C&C ADLs in terms of embedding application-speci c component behavior DSLs. This DSL enables language integrators to easily embed DSLs as required by contributing domain experts. To this e ect it exploits features of the architecture modeling infrastructure MontiArcAutomaton [ 20 ] and its underlying language workbench MontiCore [ 11 ] to alleviate reuse of the embedded DSL's syntax and semantics. This ultimately facilitates architecture modeling with domain experts.

In the following, Sect. 2 motivates the bene ts of easy, post-hoc DSL embedding before Sect. 3 highlights MontiCore and MontiArcAutomaton. Sect. 4 presents the embedding DSL and Sect. 5 highlights observations. Afterwards, Sect. 6 discusses related work and Sect. 7 concludes. 2

Example

Consider the development of an assembly line robot that sorts parts according to their quality. The robot features a camera to detect parts and means to determine their quality based on various inputs. According to their quality, the parts are sorted in di erent bins. The architecture of the system is as depicted in Fig. 1: it consists of a composed component PartSorter that yields subcomponents for sensors (Camera and QualityChecker), control, and manipulation. Components exchange messages via connectors between their typed, directed ports only and are either composed or atomic, i.e., yield a behavior speci cation in form of a DSL model or general programming language (GPL) artifact. The behavior of components Controller and Arm is provided by two respective domain experts: one being a professional in part quality and sorting that prefers to use Statecharts. The other is an expert in robot arm movement (including trajectory planning and grasping) and prefers to use the LightRocks [ 24 ] DSL for robotic manipulation. Hence, each domain expert should be enabled to use the most appropriate DSL as depicted without requiring the system integrator to become a language engineering expert. composed component type outgoing port i of coinmsptaonnceentoftyapteomwiicth data type Image embedded statechart model incoming port s of data type Skill instance of atomic component type with embedded LightRocks model

MAA PartSorter Camera Quality Checker i Image i q

Quality q

ActionController s d

Skill

Arm s d

Fig. 1. Architecture of a robot that sorts parts using two embedded DSLs.

As the e ect of behavior DSL embedding is restricted to ADL concepts `visible' in atomic components, the system integrator can leverage model-driven development to describe integration of the component behavior DSLs using a speci c integration DSL. The compact models of this DSL require minimal knowledge of the underlying language engineering principles and their representations in language workbenches. The DSL is tailored to component behavior DSL embedding into the speci c ADL and exploits this to reduce the freedom (and complexity) of arbitrary DSL composition. In this restricted context, the integrator can thus easily embed new component behavior DSLs by adding small con guration models per DSL to be embedded. The next sections present this. 3

Preliminaries

MontiCore [ 14,11 ] is a language workbench for the engineering of compositional DSLs. It generates DSL processing infrastructure from context-free grammars (CFGs), which de ne abstract syntax and concrete syntax of a DSL in an integrated fashion. MontiCore DSLs yield Java-based well-formedness rules, called context conditions, for ensured static semantics, and code generators to realize the dynamic semantics of a DSL. The generated DSL processing infrastructure comprises abstract syntax tree (AST) classes, modular parsers for each production of the CFG, a symbol table acting as language interface, and context condition infrastructure. MontiCore supports three kinds of DSL composition: embedding, inheritance, and aggregation. Embedding is purely syntactic and combines the CFGs of participating DSLs, such that their instances can be processed as integrated models. This, for instance, is useful, for embedding SQL into Java or similar. Inheritance enables one DSL to extend or re ne another via inheriting or overriding the parent CFG's productions. A typical use case is the introduction of new constructs to an existing DSL, such as the ADL ArchJava [ 1 ], which extends Java with C&C modeling elements. Aggregation enables the joint processing of models of independent DSLs, such as class diagrams describing the data types of a C&C ADL's ports.

For embedding, MontiCore supports extension points in form of external productions in the host DSL's CFG. The productions of embedded DSLs are conditionally mapped to these extension points and MontiCore combines the parsers for the corresponding productions accordingly (i.e., whenever an instance of the external production should be parsed, the corresponding embedded production is parsed instead). For aggregation, the meta models of the participating DSLs are related (for instance, to check that two ports with data types de ned in class diagrams can be connected). Instead of coupling the participating DSLs' ASTs, their symbol tables are related. Symbol tables represent DSL interfaces for speci c integration purposes and, as such, may abstract from the technical necessities of the AST classes. With aggregation, models of the participating DSL may remain in separate artifacts. For embedding with joint interpretation we apply aggregation although the models reside in integrated artifacts, hence in the following, embedding also includes joint interpretation. Instances of MontiCore's DSLTool class combine the generated parsers with context conditions and names of these methods are the keywords of BCL models incomplete

class

BCLBuilder + BCLBuilder name(String name) + BCLBuilder behavior(String behavior) + BCLBuilder tool(DSLTool tool) + BCLBuilder language(ModellingLanguage) + BCLBuilder error(ContextCondition coco) + BCLBuilder errors(ContextCondition coco) + BCLBuilder generator(MCGenerator gen) + EmbeddedDSL build() property data types are the core MontiCore classes

EmbeddedDSL - String name - MCParser parser - DSLTool tool - ModellingLanguage dsl - Map<Severity, ContextCondition> cocos - MCGenerator generator - Map<String, MCParser> embeddedDSLs symbol table infrastructure to ease programmatic model processing. DSLTool instances further can be extended with work ows that add additional model processing capabilities (such as pre-processing model-to-model transformation or automated extraction of documentation).

MontiArcAutomaton [ 20 ] is an infrastructure for model-driven development of C&C architectures that is built with MontiCore. At its core is the MontiArcAutomaton ADL [ 21 ] that enables to model architectures as hierarchies of connected components. Components are black boxes with interfaces of directed, typed ports for communication. Port types are modeled as class diagrams. Component models in MontiArcAutomaton are either composed, if their behavior is de ned by a subcomponent con guration, or atomic. The behavior of atomic components is de ned via GPL artifacts or embedded DSL models. To this e ect, MontiArcAutomaton comprises an extensible ADL that supports integration of DSLs to describe component behavior. To this extent, its CFG yields an external production used by the production for components. It also supports compositional code generation enabling transformation of such integrated models into GPL artifacts. This relies on the identi cation of three code generator kinds with speci c interfaces describing the provided and required information for composition. For the speci c context of behavior DSL embedding, these three kinds su ce to enable black-box code generator composition. 4

A DSL for Behavior Language Embedding The Behavior Con guration Language (BCL), which is responsible for con guring the embedding of component behavior DSLs into the MontiArcAutomaton ADL, is realized for usage with the language workbench MontiCore and as an internal DSL implemented in Groovy. The former entails that it uses concrete syntax referencing important concepts and artifacts of MontiCore DSLs. As MontiCore is implemented in Java and Groovy is compatible with Java, it also follows that models of the integration DSL can directly interface MontiCore artifacts.

The BCL is realized as a uent interface [ 9 ] using the builder pattern [ 10 ] to enable DSL-like usage, while actually being a Groovy GPL API. Basically, methods of the builder class become keywords of BCL models and ultimately the builder produces an instance of an embedding con guration that is used by the [single language tool] [multiple languages tool] [has ModelingLanguage]

Add language [without] [no custom symbol table] [with custom symbol table]

Add symbol table Add context conditions act Configure Embedding

Add name and behavior

[without DSLTool]

[with

DSLTool] [with adapters]

Add tool

Add generator MontiArcAutomaton infrastructure. To this e ect, it uses the MontiCore classes ContextCondition, ModelingLanguage, MCGenerator, and MCParser. The class diagram depicted in Fig. 2 illustrates this. The methods of class BCLBuilder contribute the concrete syntax and abstract syntax to describe BCL models and successively create and ll an instance of EmbeddedDSL. The BCL well-formedness rules are encoded in the build() method of BCLBuilder and, for instance, ensure that referenced grammar productions exist and minimal con guration is available. With Groovy, much of the notational noise [ 26 ] of a GPL can be omitted, hence, BCL models actually are chained calls of BCLBuilder methods with brackets and dots omitted. Omitting relations to BCLBuilder is possible as Groovy enables to process scripts in the context of so-called base classes. The Groovy parser is con gured for BCLBuilder as base class and interprets method calls in its context.

BCL models vary in complexity, which depends on the behavior DSL's `completeness' and the possibilities to derive DSL infrastructure elements from others. The activity diagram depicted in Fig. 3 describes the con guration exibility of the BCL: providing name, behavior, and generator are the only mandatory properties of each BCL model. The name speci es which production of the behavior DSLs grammar is mapped to the unique extension point in MontiArcAutomaton's grammar (cf. Sect. 3), hence each model must at least identify the grammar production (behavior) to be embedded. With MontiCore de ning concrete syntax and abstract syntax in integrated grammars, this speci cation covers embedding of both. Also, supporting usage of arbitrary productions of the behavior DSLs enables reusing required DSL parts only. As a tool in MontiCore may process multiple DSLs, it may comprise symbol table infrastructure, work ows, and context conditions for all processable DSLs. If not all DSLs of a DSLTool are to be reused, the required constituents can be speci ed individually in terms of their respective MontiCore ModelingLanguage instance [ 11 ]. If it only processes a single DSL, this DSL, together with its symbol table, work ows, and context conditions is derived from the DSLTool. Where no Application

BCL integration models

parametrizes MontiCore and generator composition with BCL instances

BCLBuilder uses

Infrastructure MAA DSLTool MAA ADL uses Generator Composition instantiates SC2Java LR2Java

SSCC GGBBCC

GPL Artifacts contain grammar, workflows, context conditions, and symbol tables references processes SC BCL

LR

BCL references SC DSL

LR DSL

ModelingLanguage is available, their constituents must be provided individually. A BCL model also may add new workflows and a code generator compatible to the behavior DSL and the code generator of the MontiArcAutomaton ADL. Where additional information is necessary (such as inter-language wellformedness rules [ 23 ]), additional modeling elements support their speci cation.

Interpretation of Groovy scripts does not require generating any infrastructure artifacts speci c to the language combination (such as combined parsers of symbol table classes), saving the expense of a generation process for each application-speci c language combination. We thus extended the infrastructure of MontiArcAutomaton (i.e., its DSLTool subclass) to initially parse all Groovy les in a MontiArcAutomaton project in the context of the BCLBuilder base class and store the EmbeddedDSL instances before processing architecture models with embedded behavior models. Overall, the MontiArcAutomaton DSLTool processes architecture models and BCL models as depicted in Fig. 4 using the BCLBuilder infrastructure. From the models, it combines the parsers generated by MontiCore for the referenced behavior DSL grammars with the MontiArcAutomaton parser and aggregates context conditions, symbol table infrastructures, and work ows. It then parses the architecture with integrated behavior models, creates symbol table instances, and applies the aggregated context conditions and work ows. Afterwards, it loads the referenced code generators and composes their execution to produce corresponding GPL artifacts.

The most compact case of DSL integration is illustrated in Lst. 1, where UML/P Statecharts [ 22 ] are integrated into the MontiArcAutomaton ADL. The integration infrastructure addresses properties of this embedding with the name \statechart" (keyword name, l. 1). It embeds the production SCBody of the Statechart DSL (keyword behavior, l. 2) and extracts context conditions, symbol table, and work ows from the DSL's SCTool (keyword tool, l. 3). Furthermore, it will use the SC2Java code generator (keyword generator, l. 4) to produce combined artifacts. The latter is a reference to a generator description artifact of BCL 1 name "statechart" 2 behavior "umlp.sc.SC.SCBody" 3 tool new umlp.sc.SCTool() 4 generator new SC2Java() 1 // abstract + concrete syntax 2 name "lightrocks" 3 behavior "lr.LightRocks.SkillDefinition" 4 tool new LightRocksTool() 5 language new SkillLanguage() 6 // inter-language well-formedness rules 7 error new ValidMotorRotation() 8 error new RespectPortDirection() 9 warning new TooManyActions() 10 // dynamic semantics 11 generator new Skill2Java() Lst. 1. BCL model that con gures the integration of the Statechart DSL into MontiArcAutomaton Lst. 2. BCL model that con gures the integration of the LightRocks Skill DSL into MontiArcAutomaton MontiArcAutomaton that describes provided and required information for code generator composition (cf. [ 20 ]).

The BCL model for the integration of the LightRocks DSL is depicted in Lst. 2. The listing begins with name, behavior, and tool (ll. 1-3). The LightRocks DSLTool supports processing of multiple DSLs for the speci cation of robotic manipulation processes, tasks, skills, and actions. As only skills are being reused within MontiArcAutomaton, the corresponding MontiCore infrastructure must be selected manually. Hence, here the DSLTool contributes work ows only, and the SkillLanguage (l. 5) contributes context conditions and symbol table infrastructure related to robotic manipulation skills. Afterwards, the model adds three inter-language context conditions (ll. 7-9) that are speci c to the integration of skills into MontiArcAutomaton components and either raise errors or warnings. Ultimately, it also adds a code generator (l. 11). Following this approach enables to process architecture models with embedded behavior models as depicted in Lst. 3, which shows the component ActionController of Fig. 1. After importing the ImageProcessor data type, it de nes the ActionController component type (ll. 3-15), which comprises an interface of three ports (ll. 4-7) and a body using an embedded Statechart model (ll. 9-14). The keyword behavior (l. 9) indicates that an embedded behavior model follows. The subsequent concrete syntax is part of the Statechart language and the tool chain con gured with the BCL model illustrated in Lst. 1 will select the SCBody parser generated by MontiCore to process it. 5

Related Work

Integrating component behavior DSLs is supported by AADL [ 8 ] and xADL [ 13 ] as well: AADL supports integration of state-based DSLs via its behavior annex [ 2 ], but does not consider integration of static semantics or dynamic semantics. The same holds for xADL, where embedding of a component behavior Lst. 3. MontiArcAutomaton component model using an embedded Statechart model to describe its behavior.

DSLs also enforces to introduce a new corresponding component type [ 18 ]. Neither supports language integration in a specialized, compact DSL.

The byADL infrastructure [ 4 ] resembles a workbench for ADLs. It supports various language integration operations that enable great exibility. However, they are generic to a broad class of application scenarios and lack the structured guidance of integration DSLs for speci c purposes.

The -ADL [ 19 ] enables to describe structure and behavior of a software architecture based on the -calculus. In this, it generally supports to add arbitrary behavior modeling capabilities on layers on top of its ADL. This, however, ultimately yields a monolithic language aggregate where the individual components can be hardly exchanged. Furthermore, it does not support black-box composition of code generators for the individual behavior DSLs.

The Kermeta language workbench enables to mashup [ 12 ] meta-languages, static semantics, and dynamic semantics to compose DSLs. This is a more general form of composition and yields the same challenges than other general composition mechanisms in other language workbenches. 6

Discussion

On the one hand, the presented BCL is speci c to its implementation with MontiArcAutomaton and MontiCore, which is re ected in its concrete syntax. On the other hand, where the integration context is su ciently elaborated, applying integration DSLs translates to other language workbenches as well. A bene t of combining MontiCore with an internal integration DSL is in its agility: AST classes and parsers generated for ADL and behavior DSLs can be reused without requiring intermediate code generation to produce integrated AST classes and parsers (cf. [ 11 ]). Also, no artifacts are produced from the integration model, which is validated in the context of related MontiCore artifacts at design time. Our notion of DSL embedding di ers from the self-extension identi ed in [ 7 ] and discussed in [ 11 ]. Where we advocate embedding of independent, external DSLs, self-extension usually refers to internal DSLs that can easily extend their concrete syntax with new APIs. However, with the embedding of su cient complex DSLs (such as the Java DSL presented in [ 23 ]), self-extension can be realized with embedding. The BCL relieves language engineers from detailed knowledge about the MontiCore language composition mechanisms. The embedding of a new behavior DSL takes place in a single BCL model, instead of registering DSL infrastructure across scattered artifacts. Embedding is decoupled from the DSLs, enabling to reuse behavior DSLs in di erent contexts.

7 Conclusion

We have presented a small DSL for the agile adaptation of C&C ADLs via embedding of component behavior DSLs. It supports ADL developers in tting their ADL to the participating domain experts' DSL requirements and supports agile, post-hoc customization exploiting properties of the underlying MontiCore language workbench as well as its internal DSL nature. In the future, similarly elaborated language integration purposes might produce further speci c integration DSLs (for instance, to easily integrate and exchange the data type description language an ADL operates on) for di erent purposes and on top of di erent language workbenches. We believe that such specialized integration operations can advance the application of model-driven development, where adaptation of existing DSLs is crucial.

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