Textual Domain-specific Languages Research Articles

Design Goals for End-User Development of Robot-Assisted Physical Training Activities: A Participatory Design Study

Programming robots presents significant challenges, including high costs, extensive time commitments and steep learning curves, particularly for individuals lacking technical background in engineering. These barriers have been partially mitigated by the emergence of end-user development methodologies. Yet existing approaches often fall short in equipping users with the necessary software engineering competencies to develop comprehensive robot behaviors or to effectively maintain and re-purpose their creations. In this paper, we introduce a novel end-user development approach designed to empower physical therapists to independently specify robot-assisted physical training exercises, eliminating the need for robotics experts' intervention. Our approach is based on a set of design goals obtained through a participatory design study with experts in the field. It utilizes a textual domain-specific language (DSL) that enables users to define expected robot behaviors through Behaviour-Driven Development (BDD) scenarios. This paper discusses key themes, design objectives, and the evolution of requirements that emerged from an evaluative workshop.

Supporting meta-model-based language evolution and rapid prototyping with automated grammar transformation

In model-driven engineering, developing a textual domain-specific language (DSL) involves constructing a meta-model, which defines an underlying abstract syntax, and a grammar, which defines the concrete syntax for the DSL. We consider a scenario in which the meta-model is manually maintained, which is common in various contexts, such as blended modeling, in which several concrete syntaxes co-exist in parallel. Language workbenches such as Xtext support such a scenario, but require the grammar to be manually co-evolved, which is laborious and error-prone. In this paper, we present GrammarTransformer, an approach for transforming generated grammars in the context of meta-model-based language evolution. To reduce the effort for language engineers during rapid prototyping and language evolution, it offers a catalog of configurable grammar transformation rules. Once configured, these rules can be automatically applied and re-applied after future evolution steps, greatly reducing redundant manual effort. In addition, some of the supported transformations can globally change the style of concrete syntax elements, further significantly reducing the effort for manual transformations. The grammar transformation rules were extracted from a comparison of generated and existing, expert-created grammars, based on seven available DSLs. An evaluation based on the seven languages shows GrammarTransformer’s ability to modify Xtext-generated grammars in a way that agrees with manual changes performed by an expert and to support language evolution in an efficient way, with only a minimal need to change existing configurations over time.

The OpenESEA Modeling Language and Tool for Ethical, Social, and Environmental Accounting

Assessing business operations’ ethical, social, and environmental impacts is a key practice for establishing sustainable development. There is a multitude of methods that describes how to perform such assessments. Often these methods are supported by an ICT tool. In most cases, the tools are developed to support a single method only and do not allow any tailoring. Therefore, they are rigid and inflexible. In this article, we present a novel model-driven approach for alleviating managerial issues that arise as a consequence of the complex landscape of ethical, social, and environmental accounting methods and tools. We have developed an open-source, model-driven tool, called openESEA. OpenESEA parses and interprets textual models, that are specified according to a domain-specific language (DSL). We have performed another iteration of the DSL engineering process, which is in line with the design science paradigm. We have validated the new DSL version by means of a user study. As a result, we present a new version of the openESEA modeling language and interpreter. The results of the user study with regards to performance, perceived usefulness, and perceived ease of use of modeling language are encouraging and provide us with a basis to continue developing new versions with more functionalities. The contributions of this work include a new version of the modeling language, a new version of the interpreter, knowledge surrounding the development of these artifacts, and a protocol for evaluating the quality of textual DSLs. The modeling language and interpreter are relevant for sustainability practitioners and consultants since our tool support has the potential to reduce redundancy in ethical, social, and environmental accounting. Our work is valuable to researchers that aim to assess and reduce the complexity of their modeling languages.

SEMKIS-DSL: A Domain-Specific Language to Support Requirements Engineering of Datasets and Neural Network Recognition

Neural network (NN) components are being increasingly incorporated into software systems. Neural network properties are determined by their architecture, as well as the training and testing datasets used. The engineering of datasets and neural networks is a challenging task that requires methods and tools to satisfy customers’ expectations. The lack of tools that support requirements specification languages makes it difficult for engineers to describe dataset and neural network recognition skill requirements. Existing approaches often rely on traditional ad hoc approaches, without precise requirement specifications for data selection criteria, to build these datasets. Moreover, these approaches do not focus on the requirements of the neural network’s expected recognition skills. We aim to overcome this issue by defining a domain-specific language that precisely specifies dataset requirements and expected recognition skills after training for an NN-based system. In this paper, we present a textual domain-specific language (DSL) called SEMKIS-DSL (Software Engineering Methodology for the Knowledge management of Intelligent Systems) that is designed to support software engineers in specifying the requirements and recognition skills of neural networks. This DSL is proposed in the context of our general SEMKIS development process for neural network engineering. We illustrate the DSL’s concepts using a running example that focuses on the recognition of handwritten digits. We show some requirements and recognition skills specifications and demonstrate how our DSL improves neural network recognition skills.

MIKADO: a smart city KPIs assessment modeling framework

Smart decision making plays a central role for smart city governance. It exploits data analytics approaches applied to collected data, for supporting smart cities stakeholders in understanding and effectively managing a smart city. Smart governance is performed through the management of key performance indicators (KPIs), reflecting the degree of smartness and sustainability of smart cities. Even though KPIs are gaining relevance, e.g., at European level, the existing tools for their calculation are still limited. They mainly consist in dashboards and online spreadsheets that are rigid, thus making the KPIs evolution and customization a tedious and error-prone process. In this paper, we exploit model-driven engineering (MDE) techniques, through metamodel-based domain-specific languages (DSLs), to build a framework called MIKADO for the automatic assessment of KPIs over smart cities. In particular, the approach provides support for both: (i) domain experts, by the definition of a textual DSL for an intuitive KPIs modeling process and (ii) smart cities stakeholders, by the definition of graphical editors for smart cities modeling. Moreover, dynamic dashboards are generated to support an intuitive visualization and interpretation of the KPIs assessed by our KPIs evaluation engine. We provide evaluation results by showing a demonstration case as well as studying the scalability of the KPIs evaluation engine and the general usability of the approach with encouraging results. Moreover, the approach is open and extensible to further manage comparison among smart cities, simulations, and KPIs interrelations.

Touch: A Textual Programming Language for Developing APPs of Insect Intelligent Building

Insect intelligent building (I2B) is a novel decentralized, flat-structured intelligent building platform with excellent flexibility and scalability. I2B allows users to develop applications that include control strategies for efficiently managing and controlling buildings. However, developing I2B APPs (applications) is considered a challenging and complex task due to the complex structural features and parallel computing models of the I2B platform. Existing studies have been shown to encounter difficulty in supporting a high degree of abstraction and in allowing users to define control scenarios in a concise and comprehensible way. This paper aims to facilitate the development of such applications and to reduce the programming difficulty. We propose Touch, a textual domain-specific language (DSL) that provides a high-level abstraction of I2B APPs. Specifically, we first establish the conceptual programming architecture of the I2B APP, making the application more intuitive by abstracting different levels of physical entities in I2B. Then, we present special language elements to effectively support the parallel computing model of the I2B platform and provide a formal definition of the concrete Touch syntax. We also implement supporting tools for Touch, including a development environment as well as target code generation. Finally, we present experimental results to demonstrate the effectiveness and efficiency of Touch.

INDIeAuthor: A Metamodel-Based Textual Language for Authoring Educational Courses

This paper presents the INDIeAuthor authoring tool, which has been developed at the Digital Content Production Center, Polytechnic University of Cartagena (UPCT). INDIeAuthor has been developed in order for the university to have its own tool that supports all the desired features, in addition to a platform with which to investigate innovative features. When building INDIeAuthor, we have overcome some limitations identified for existing authoring tools: 1) Lack of two essential content reuse mechanisms: defined-user templates and course-independent units; 2) No support to sequence the units on a course; 3) Gamification feature is either very limited or does not exist. Two new aspects of the proposal are: providing a family of four textual domain-specific languages rather than a graphical user interface and applying model-based software engineering techniques during the implementation of the languages. Four essential aspects in the course definition can be specified to the language family: content, assessment, gamification, and sequencing. We discuss the benefits of representing courses as models and present two utilities developed as a proof of concept. This paper also contributes with the definition of a feature model that establishes a conceptual framework in which to compare authoring tools. An evaluation of INDIeAuthor is also presented: a case study was carried to evaluate the language characteristics, and the tool is contrasted with eight widely-used authoring tools. This paper presented here is the baseline of INDIe Erasmus+ European project that is currently ongoing.

Syntactic Parsing Algorithms for Textual Domain-Specific Languages

Syntactic Parsing Algorithms for Textual Domain-Specific Languages

AocML: A Domain-Specific Language for Model-Driven Development of Activity-Oriented Context-Aware Applications

Activity-oriented context-aware (AOCA) applications are representative in pervasive computing. These applications recognize daily-life human activities, perceive the environment status related to the activities, and react to ensure the smooth performance of the activities. Existing research proposed a specific light-weight, incremental method to support the development of such applications; however it is not easy to learn and use. This paper aims to facilitate the development of such applications and improve the productivity of developers. We propose AocML, a textual domain-specific language which provides a high-level abstraction of AOCA applications. Specifically, we first show the software model of AOCA applications and the abstract syntax of AocML. Then, we introduce the concrete syntax of AocML. We also implement the tools for AocML, including the development environment as well as the generation of Java code and ontology specification. Moreover, we use a case study and evaluation to demonstrate the advantages of AocML.

Automatic production of end user documentation for DSLs

Domain-specific languages (DSL) are developed for answering specific problems by leveraging the expertise of domain stakeholders. 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. 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.

Establishing User-centric Cloud Service Registries

Many potential cloud consumers are overburdened by the challenges persisting when discovering, assessing, and selecting contemporary Cloud Service offerings: the cloud market is vast and fast-moving, the selection criteria are ambiguous, service knowledge is scattered through the Internet, and features as well as prices are complex and incomparable. Much research has been carried out to create cloud service registries to help users select cloud services for eventual consumption, especially within the field of semantic web services. Through analyzing real-world requirements of six use cases we identified a gap in research for user-centric technologies. We fill this gap by creating a business vocabulary reflecting common service selection criteria, defining a textual domain specific language to let any user describe services easily, and implementing a novel brokering and matchmaking component to support users in their selection process. As a combination of those technologies, we create the Open Service Compendium (OSC), a crowd-sourced cloud service registry. Our evaluation activities highlight how these developments solve real-world challenges in diverse near-production settings. All of this implies that a substantial benefit for service registry users can be created by following a simple architecture that is focused on their concrete needs — instead of aiming for highest sophistication and broadest applicability as observed in many of the related works.

Toward live domain-specific languages

Live programming is a style of development characterized by incremental change and immediate feedback. Instead of long edit-compile cycles, developers modify a running program by changing its source code, receiving immediate feedback as it instantly adapts in response. In this paper, we propose an approach to bridge the gap between running programs and textual domain-specific languages (DSLs). The first step of our approach consists of applying a novel model differencing algorithm, tmdiff, to the textual DSL code. By leveraging ordinary text differencing and origin tracking, tmdiff produces deltas defined in terms of the metamodel of a language. In the second step of our approach, the model deltas are applied at run time to update a running system, without having to restart it. Since the model deltas are derived from the static source code of the program, they are unaware of any run-time state maintained during model execution. We therefore propose a generic, dynamic patch architecture, rmpatch, which can be customized to cater for domain-specific state migration. We illustrate rmpatch in a case study of a live programming environment for a simple DSL implemented in Rascal for simultaneously defining and executing state machines.

A model-driven approach for quality of context in pervasive systems

Handling context is a crucial activity in context-aware systems. In building such systems, the creation of models helps developers to understand and reason on the context information. The quality of context information is required to achieve these systems behavior according to the requirements. Therefore, context models should not only represent the context information but also the quality of context (QoC). MLContext is a textual domain-specific language (DSL) designed to model context, which has been used to automatically generate software artifacts related to the context management for some context-aware frameworks. In this article we present an MLContext extension for modeling QoC. The QoC added features include constructs to express context situations (i.e. constraints on context information), quality requirements and quality levels. The new constructs have been mapped to code for two frameworks supporting QoC (COSMOS and SAMURAI). These mappings have been validated by means of a case study.

Quantifying usability of domain-specific languages: An empirical study on software maintenance

A domain-specific language (DSL) aims to support software development by offering abstractions to a particular domain. It is expected that DSLs improve the maintainability of artifacts otherwise produced with general-purpose languages. However, the maintainability of the DSL artifacts and, hence, their adoption in mainstream development, is largely dependent on the usability of the language itself. Unfortunately, it is often hard to identify their usability strengths and weaknesses early, as there is no guidance on how to objectively reveal them. Usability is a multi-faceted quality characteristic, which is challenging to quantify beforehand by DSL stakeholders. There is even less support on how to quantitatively evaluate the usability of DSLs used in maintenance tasks. In this context, this paper reports a study to compare the usability of textual DSLs under the perspective of software maintenance. A usability measurement framework was developed based on the cognitive dimensions of notations. The framework was evaluated both qualitatively and quantitatively using two DSLs in the context of two evolving object-oriented systems. The results suggested that the proposed metrics were useful: (1) to early identify DSL usability limitations, (2) to reveal specific DSL features favoring maintenance tasks, and (3) to successfully analyze eight critical DSL usability dimensions.

Air-Combat Decision Modeling Method Based on DSM

Air-combat decision modeling in effectiveness simulation has to be concerned with the important feature of decision making, such as complexity, diversity, flexibility. So Several challenges have to be mastered, including: improving the abstract level of modeling, providing friendly modeling language, validating concept model and generated code (or executive model) automatically. In this paper, domain-specific modeling (DSM) method is applied in air-combat decision simulation modeling to cope with those challenges. A graphical and textual domain-specific modeling language (DSML) of air-combat decision is designed through metamodel based on an open source tool, Generic Modeling Environment (GME). A code generator is developed to implement users decision model based on python script.

A domain-specific language for context modeling in context-aware systems

Context-awareness refers to systems that can both sense and react based on their environment. One of the main difficulties that developers of context-aware systems must tackle is how to manage the needed context information. In this paper we present MLContext, a textual Domain-Specific Language (DSL) which is specially tailored for modeling context information. It has been implemented by applying Model-Driven Development (MDD) techniques to automatically generate software artifacts from context models. The MLContext abstract syntax has been defined as a metamodel, and model-to text transformations have been written to generate the desired software artifacts. The concrete syntax has been defined with the EMFText tool, which generates an editor and model injector.MLContext has been designed to provide a high-level abstraction, to be easy to learn, and to promote reuse of context models. A domain analysis has been applied to elicit the requirements and design choices to be taken into account in creating the DSL. As a proof of concept of the proposal, the generative approach has been applied to two different middleware platforms for context management.

Xbase

Xtext is an open-source framework for implementing external, textual domain-specific languages (DSLs). So far, most DSLs implemented with Xtext and similar tools focus on structural aspects such as service specifications and entities. Because behavioral aspects are significantly more complicated to implement, they are often delegated to general-purpose programming languages. This approach introduces complex integration patterns and the DSL's high level of abstraction is compromised. We present Xbase as part of Xtext, an expression language that can be reused via language inheritance in any DSL implementation based on Xtext. Xbase expressions provide both control structures and program expressions in a uniform way. Xbase is statically typed and tightly integrated with the Java type system. Languages extending Xbase inherit the syntax of a Java-like expression language as well as language infrastructure components, including a parser, an unparser, a linker, a compiler and an interpreter. Furthermore, the framework provides integration into the Eclipse IDE including debug and refactoring support. The application of Xbase is presented by means of a domain model language which serves as a tutorial example and by the implementation of the programming language Xtend. Xtend is a functional and object-oriented general purpose language for the Java Virtual Machine (JVM). It is built on top of Xbase which is the reusable expression language that is the foundation of Xtend.

The spoofax language workbench

Spoofax is a language workbench for efficient, agile development of textual domain-specific languages with state-of-the-art IDE support. Spoofax integrates language processing techniques for parser generation, meta-programming, and IDE development into a single environment. It uses concise, declarative specifications for languages and IDE services. In this paper we describe the architecture of Spoofax and introduce idioms for high-level specifications of language semantics using rewrite rules, showing how analyses can be reused for transformations, code generation, and editor services such as error marking, reference resolving, and content completion. The implementation of these services is supported by language-parametric editor service classes that can be dynamically loaded by the Eclipse IDE, allowing new languages to be developed and used side-by-side in the same Eclipse environment.

MontiCore: a framework for compositional development of domain specific languages

Domain specific languages (DSLs) are increasingly used today. Coping with complex language definitions, evolving them in a structured way, and ensuring their error freeness are the main challenges of DSL design and implementation. The use of modular language definitions and composition operators are therefore inevitable in the independent development of language components. In this article, we discuss these arising issues by describing a framework for the compositional development of textual DSLs and their supporting tools. We use a redundance-free definition of a readable concrete syntax and a comprehensible abstract syntax as both representations significantly overlap in their structure. For enhancing the usability of the abstract syntax, we added concepts like associations and inheritance to a grammar-based definition in order to build up arbitrary graphs (as known from metamodeling). Two modularity concepts, grammar inheritance and embedding, are discussed. They permit compositional language definition and thus simplify the extension of languages based on already existing ones. We demonstrate that compositional engineering of new languages is a useful concept when project-individual DSLs with appropriate tool support are defined.

Generating safe template languages

Template languages are widely used within generative programming, because they provide intuitive means to generate software artefacts expressed in a specific object language. However, most template languages perform template instantiation on the level of string literals, which allows neither syntax checks nor semantics analysis. To make sure that generated artefacts always conform to the object language, we propose to perform static analysis at template design time. In addition, the increasing popularity of domainspecific languages (DSLs) demands an approach that allows to reuse both the concepts of template languages and the corresponding tools. In this paper we address the issues mentioned above by presenting how existing languages can be automatically extended with generic template concepts (e.g., placeholders, loops, conditions) to obtain safe template languages. These languages provide means for syntax checking and static semantic analysis w.r.t. the object language at template design time. We discuss the prerequisites for this extension, analyse the types of correctness properties that can be assured at template design time, and exemplify the key benefits of this approach on a textual DSL and Java.