Section: New Results

Results on Software Language Engineering

Omniscient Debugging for Executable DSLs

Omniscient debugging is a promising technique that relies on execution traces to enable free traversal of the states reached by a model (or program) during an execution. While a few General-Purpose Languages (GPLs) already have support for omniscient debugging, developing such a complex tool for any executable Domain Specific Language (DSL) remains a challenging and error prone task. A generic solution must: support a wide range of executable DSLs independently of the metaprogramming approaches used for implementing their semantics; be efficient for good responsiveness. Our contribution in [21] relies on a generic omniscient debugger supported by efficient generic trace management facilities. To support a wide range of executable DSLs, the debugger provides a common set of debugging facilities, and is based on a pattern to define runtime services independently of metaprogramming approaches. Results show that our debugger can be used with various executable DSLs implemented with different metaprogramming approaches. As compared to a solution that copies the model at each step, it is on average six times more efficient in memory, and at least 2.2 faster when exploring past execution states, while only slowing down the execution 1.6 times on average.

Trace Comprehension Operators for Executable DSLs

Recent approaches contribute facilities to breathe life into metamodels, thus making behavioral models directly executable. Such facilities are particularly helpful to better utilize a model over the time dimension, e.g., for early validation and verification. However, when even a small change is made to the model, to the language definition (e.g., semantic variation points), or to the external stimuli of an execution scenario, it remains difficult for a designer to grasp the impact of such a change on the resulting execution trace. This prevents accessible trade-off analysis and design-space exploration on behavioral models. In [44], we propose a set of formally defined operators for analyzing execution traces. The operators include dynamic trace filtering, trace comparison with diff computation and visualization, and graph-based view extraction to analyze cycles. The operators are applied and validated on a demonstrative example that highlight their usefulness for the comprehension specific aspects of the underlying traces.

Model Transformation Reuse across Metamodels

Model transformations (MTs) are essential elements of model-driven engineering (MDE) solutions. MDE promotes the creation of domain-specific metamodels, but without proper reuse mechanisms, MTs need to be developed from scratch for each new metamodel. In [32], awarded by the best paper award at ICMT 2018, we classify reuse approaches for MTs across different metamodels and compare a sample of specific approaches – model types, concepts, a-posteriori typing, multilevel modeling, and design patterns for MTs – with the help of a feature model developed for this purpose, as well as a common example. We discuss strengths and weaknesses of each approach, provide a reading grid used to compare their features, and identify gaps in current reuse approaches.

Modular Language Composition for the Masses

The goal of modular language development is to enable the definition of new languages as assemblies of pre-existing ones. Recent approaches in this area are plentiful but usually suffer from two main problems: either they do not support modular language composition both at the specification and implementation levels, or they require advanced knowledge of specific paradigms which hampers wide adoption in the industry. In [36], awarded by the best artefact award at SLE 2018, we introduce a non-intrusive approach to modular development of language concerns with well-defined interfaces that can be composed modularly at the specification and implementation levels. We present an implementation of our approach atop the Eclipse Modeling Framework, namely Alex-an object-oriented metalanguage for semantics definition and language composition. We evaluate Alex in the development of a new DSL for IoT systems modeling resulting from the composition of three independently defined languages (UML activity diagrams, Lua, and the OMG Interface Description Language). We evaluate the effort required to implement and compose these languages using Alex with regards to similar approaches of the literature.

Shape-Diverse DSLs

Domain-Specific Languages (DSLs) manifest themselves in remarkably diverse shapes, ranging from internal DSLs embedded as a mere fluent API within a programming language, to external DSLs with dedicated syntax and tool support. Although different shapes have different pros and cons, combining them for a single language is problematic: language designers usually commit to a particular shape early in the design process, and it is hard to reconsider this choice later. In the new ideas paper [33] awarded as the best new ideas paper at SLE 2018, we envision a language engineering approach enabling (i) language users to manipulate language constructs in the most appropriate shape according to the task at hand, and (ii) language designers to combine the strengths of different technologies for a single DSL. We report on early experiments and lessons learned building Prism, our prototype approach to this problem. We illustrate its applicability in the engineering of a simple shape-diverse DSL implemented conjointly in Rascal, EMF, and Java. We hope that our initial contribution will raise the awareness of the community and encourage future research.

Fostering metamodels and grammars

Advanced and mature language workbenches have been proposed in the past decades to develop Domain-Specific Languages (DSL) and rich associated environments. They all come in various flavors, mostly depending on the underlying technological space (e.g., grammarware or modelware). However, when the time comes to start a new DSL project, it often comes with the choice of a unique technological space which later bounds the possible expected features. In [37], we introduce NabLab, a full-fledged industrial environment for scientific computing and High Performance Computing (HPC), involving several metamodels and grammars. Beyond the description of an industrial experience of the development and use of tool-supported DSLs, we report in this paper our lessons learned, and demonstrate the benefits from usefully combining metamodels and grammars in an integrated environment.

Automatic Production of End User Documentation for DSLs

The development of DSLs requires a significant software engineering effort: editors, code generators, etc., must be developed to make a DSL usable. Documenting a DSL is also a major and time-consuming task required to promote it and address its learning curve. Recent research work in software language engineering focus on easing the development of DSLs. This work focuses on easing the production of documentation of textual DSLs [27], [17]. The API documentation domain identified challenges we adapted to DSL documentation. Based on these challenges we propose a model-driven approach that relies on DSL artifacts to extract information required to build documentation. Our implementation, called Docywood, targets two platforms: Markdown documentation for static web sites and Xtext code fragments for live documentation while modeling. We used Docywood on two DSLs, namely ThingML and Target Platform Definition. Feedback from end users and language designers exhibits qualitative benefits of the proposal with regard to the DSL documentation challenges. End user experiments conducted on ThingML and Target Platform Definition show benefits on the correctness of the created models when using Docywood on ThingML.