Search

Textual editors are omnipresent in all software tools. Editors provide basic features, such as copy-pasting and searching, or more advanced features, such as error checking and text completion. Current technologies in model-driven engineering can automatically generate textual editors to manipulate domain-specific languages (DSLs). However, the customization and addition of new features to these editors is often limited to changing the internal structure and behavior. In this paper, we explore a new generation of self-descriptive textual editors for DSLs, allowing full configuration of their structure and behavior in a convenient formalism, rather than in source code. We demonstrate the feasibility of the approach by providing a prototype implementation and applying it in two domain-specific modeling scenarios, including one in architecture modeling.

Globalization has increased the need for society to master new languages. This need has encouraged the launch of many applications dedicated to language learning. This paper presents a graphical notation for a domain specific language to represent language learning activities. It describes how this notation enables developers to represent language learning activity characteristics using workflow, presentation, content, media and activity model conforming a metamodel that defines the abstract syntax of the domain specific language. This notation is implemented as part of an integrated development environment to build model-based applications. Finally, this proposal is evaluated with a framework that uses the cognitive dimensions of notations for notational systems. The proposed graphic diagram editor exceeds the experience that the user has with the reflexive model editor. In relation to the creation and editing of workflow models and presentation/activity models, the proposed graphical notation its more intuitive and easy to maintain visually than the traditional reflexive tree notation used by many model-based development frameworks.

High-resolution simulations can deliver great visual quality, but they are often limited by available memory, especially on GPUs. We present a compiler for physical simulation that can achieve both high performance and significantly reduced memory costs, by enabling flexible and aggressive quantization. Low-precision ("quantized") numerical data types are used and packed to represent simulation states, leading to reduced memory space and bandwidth consumption. Quantized simulation allows higher resolution simulation with less memory, which is especially attractive on GPUs. Implementing a quantized simulator that has high performance and packs the data tightly for aggressive storage reduction would be extremely labor-intensive and error-prone using a traditional programming language. To make the creation of quantized simulation practical, we have developed a new set of language abstractions and a compilation system. A suite of tailored domain-specific optimizations ensure quantized simulators often run as fast as the full-precision simulators, despite the overhead of encoding-decoding the packed quantized data types. Our programming language and compiler, based on Taichi , allow developers to effortlessly switch between different full-precision and quantized simulators, to explore the full design space of quantization schemes, and ultimately to achieve a good balance between space and precision. The creation of quantized simulation with our system has large benefits in terms of memory consumption and performance, on a variety of hardware, from mobile devices to workstations with high-end GPUs. We can simulate with levels of resolution that were previously only achievable on systems with much more memory, such as multiple GPUs. For example, on a single GPU, we can simulate a Game of Life with 20 billion cells (8× compression per pixel), an Eulerian fluid system with 421 million active voxels (1.6× compression per voxel), and a hybrid Eulerian-Lagrangian elastic object simulation with 235 million particles (1.7× compression per particle). At the same time, quantized simulations create physically plausible results. Our quantization techniques are complementary to existing acceleration approaches of physical simulation: they can be used in combination with these existing approaches, such as sparse data structures, for even higher scalability and performance.

In this work, we make the case for Python as a first-class language for high-performance computing (HPC) applications. Our rationale is based on three trends: an increasing desire for applications to be user friendly; the shift toward such applications becoming primarily consumers of third-party application programming interfaces; and growing use of code generation for performance sensitive kernels. In each instance, we argue why Python is better placed to ride these trends than traditional languages such as C or Fortran. Further, we will also address two additional concerns regarding the suitability of Python: long start-up times and limited support for multithreading. An as exemplar of Python in HPC, we consider the high-order computational fluid dynamics code PyFR. The novel architecture of PyFR is described including examples showcasing its domain-specific language.

Multi-agent systems have evolved with their application framework, analysis approaches and design complexities over the past few decades. In order for programming software agents, you need to handle analysis, design and implementation together; furthermore, privacy and trust should be integrated externally into the agent-oriented applications. Current frameworks do not consider privacy, trust, and data integrity in agent applications. In this study, we propose a domain-specific language that can be used for agent behaviors that consist of roles, organizational entity, goals through annotation processing with templates. Smart contracts can be generated to decrease the time for deployment and development stages. Templates and annotations are popular techniques to reduce boilerplate codebases from agent-oriented programming. These techniques can also be used for model-driven software engineering. This study will take the software agent development as a whole with analysis, design, and development with embedded domain-specific language development in terms of smart contract applications. Furthermore, we would like to refer to methodology, results of the research, and case study to enlighten readers in a better way. Finally, we summarize findings and highlight the main research points by inferencing in the conclusion section.

This article presents a framework, Genesis (genome analysis), to efficiently and flexibly accelerate generic data manipulation operations that have become performance bottlenecks in the genomic data processing pipeline utilizing FPGAs-as-a-service. Genesis conceptualizes genomic data as a very large relational database and uses extended SQL as a domain-specific language to construct data manipulation queries. To accelerate the queries, we designed a Genesis hardware library of efficient coarse-grained primitives that can be composed into a specialized dataflow architecture. This approach explores a systematic and scalable methodology to expedite domain-specific end-to-end accelerated system development and deployment.

Cyber attacks on IT and OT systems can have severe consequences for individuals and organizations, from water or energy distribution systems to online banking services. To respond to these threats, attack simulations can be used to assess the cyber security of systems to foster a higher degree of resilience against cyber attacks; the steps taken by an attacker to compromise sensitive system assets can be traced, and a time estimate can be computed from the initial step to the compromise of assets of interest. Previously, the Meta Attack Language (MAL) was introduced as a framework to develop security-oriented domain-specific languages. It allows attack simulations on modeled systems and analyzes weaknesses related to known attacks. To produce more realistic simulation results, probability distributions can be assigned to attack steps and defenses to describe the efforts required for attackers to exploit certain attack steps. However, research on assessing such probability distributions is scarce, and we often rely on security experts to model attackers’ efforts. To address this gap, we propose a method to assign probability distributions to the attack steps and defenses of MAL-based languages. We demonstrate the proposed method by assigning probability distributions to a MAL-based language. Finally, the resulting language is evaluated by modeling and simulating a known cyber attack

U radu je predstavljena integracija NewWave workflow engine i OpenPonk alata. Model je nacrtan u OpenPonk editoru za meta-modelovanje. Plugin je zadužen za parsiranje modela u jeziku specifičnom za domen i mapiranje na NewWave elemente.

Domain adaptation in sentiment analysis is one of the areas where a classifier trained in one domain often classifies sentiments poorly when applied to another domain due to domain-specific words. Extracting features and their relevant opinion words from different domain sources and mapping them to the target domains are herculean tasks as far as domain adaptation is concerned. In this paper, the feature extraction technique is refined by which the mapping task is enhanced. The feature extraction technique uses both the syntactic and semantic properties of the features for extracting similar words. The features are further refined by merging synonyms and by replacing negative polarity terms with the appropriate antonyms. This refinement in the feature selection improves the mapping functionality of the domain adaptation and also exploits the relationship between domain-specific words and domain-independent words from different domains.

Both music creation tools and novice coding environments often use highly visual interfaces to aid in writing, editing, and navigation. Such designs fail to include blind users and may be totally inaccessible. This work sets out to develop a novel domain specific language, interface, and curriculum in partnership with students and teachers at the Filomen M. D'Agostino Greenberg (FMDG) School, a community music school that reaches blind and visually impaired musicians of all ages and skill levels. The project will consist of an iterative co-design phase and an implementation/evaluation phase in which a small number of students at the school will use the technology during a music composition course to create original works.

Low code development environments are gaining attention due to their potential as a development paradigm for very large scale adoption in the future IT. In this paper, we propose a method to extend the (application) Domain Specific Languages supported by two low code development environments based on formal models, namely DIME (native Java) and Pyro (native Python), to include functionalities hosted on heterogeneous technologies and platforms. For this we follow the analogy of micro services. After this integration, both environments can leverage the communication with pre-existing remote RESTful and enterprise systems’ services, in our case Amazon Web Services (AWS) (but this can be easily generalized to other cloud platforms). Developers can this way utilize within DIME and Pyro the potential of sophisticated services, potentially the entire Python and AWS ecosystems, as libraries of drag and drop components in their model driven, low-code style. The new DSLs are made available in DIME and Pyro as collections of implemented SIBs and blocks. Due to the specific capabilities and checks underlying the DIME and Pyro platforms, the individual DSL functionalities are automatically validated for semantic and syntactical errors in both environments.

Static analysis is an essential way to find code smells and bugs. It checks the source code without execution and no test cases are required, therefore its cost is lower than testing. Moreover, static analysis can help in software engineering comprehensively, since static analysis can be used for the validation of code conventions, for measuring software complexity and for executing code refactorings as well. Symbolic execution is a static analysis method where the variables (e.g. input data) are interpreted with symbolic values. Clang Static Analyzer is a powerful symbolic execution engine based on the Clang compiler infrastructure that can be used with C, C++ and Objective-C. Validation of resources’ usage (e.g. files, memory) requires finite state automata (FSA) for modeling the state of resource (e.g. locked or acquired resource). In this paper, we argue for an approach in which automata are in-use during symbolic execution. The generic automaton can be customized for different resources. We present our domain-specific language to define automata in terms of syntactic and semantic rules. We have developed a tool for this approach which parses the automaton and generates Clang Static Analyzer checker that can be used in the symbolic execution engine. We show an example automaton in our domain-specific language and the usage of generated checker.

Macros provide a powerful means of extending languages. They have proven useful in both general-purpose and domain-specific programming contexts. This paper presents an architecture for implementing macro-extensible DSLs on top of macro-extensible host languages. The macro expanders of these DSLs inherit the syntax system, hygienic expansion, and more from the host. They transform the extensible DSL syntax into a DSL core language. This arrangement has several important consequences. It becomes straightforward to integrate the syntax of various DSLs and the host language when their expanders share these inherited components. Also, a DSL compiler may be designed around a fixed core language, even for an extensible DSL. Finally, macros empower programmers to safely grow DSLs on their own and tailor them to their needs.

A robot’s code needs to sense the environment, control the hardware, and communicate with other robots. Current programming languages do not provide suitable abstractions that are independent of hardware platforms. Currently, developing robot applications requires detailed knowledge of signal processing, control, path planning, network protocols, and various platform-specific details. Further, porting applications across hardware platforms remains tedious. We present Koord—a domain specific language for distributed robotics—which abstracts platform-specific functions for sensing, communication, and low-level control. Koord makes the platform-independent control and coordination code portable and modularly verifiable. Koord raises the level of abstraction in programming by providing distributed shared memory for coordination and port interfaces for sensing and control. We have developed the formal executable semantics of Koord in the K framework. With this symbolic execution engine, we can identify assumptions (proof obligations) needed for gaining high assurance from Koord applications. We illustrate the power of Koord through three applications: formation flight, distributed delivery, and distributed mapping. We also use the three applications to demonstrate how platform-independent proof obligations can be discharged using the Koord Prover while platform-specific proof obligations can be checked by verifying the obligations using physics-based models and hybrid verification tools.

As a part of domain-specific development, Domain-Specific Language (DSL) is widely used in both the academia and industry to solve different aspects of the problems in engineering. A DSL is a customized language whose expressiveness is tailored to a well-defined application domain, so as to offer an effective interface for the domain experts. To mitigate the programming complexity of the General-Purpose Programming Languages, and meanwhile maintain the precise expression towards some exact engineering domains, DSLs present a higher level of abstraction than low-level interfaces, while providing much more flexibility than high-level interfaces. Nevertheless, it lacks a survey to have a systematic overview of the essential commonalities shared by those works. In this survey, we take a brand-new perspective, to categorize the state-of-the-art works into different categories, tailored to three fundamental implementation concerns of DSLs: abstract syntax, concrete syntax, and semantics. Specifically, they are characterized according to their parsing and mapping strategy (external/internal) between the abstract syntax and concrete syntax, the mapping results (textual/graphical symbols), and also the functions they emphasize (modeling, visualizing, etc.). Integrated with the literature, we finally summarized the research overview of DSLs.

Ad hoc code generation is a state-of-the-art processing paradigm for database execution engines. It minimizes resource consumption by generating specialized code, tailored and streamlined for the single query at hand. In this work, we apply ad hoc code generation to regular path queries (RPQs), an advanced query type in declarative graph query languages. We investigate code generation from multiple angles. We propose COAT, an embedded domain specific language (EDSL) in C++ to improve accessibility of code generation by simplifying the interaction with compiler APIs. Furthermore, we analyze and compare two back ends for COAT providing the just-in-time (JIT) compilation functionality: LLVM, a compiler framework popularly used in databases for code generation, and AsmJit, a JIT assembler with very low compilation latency. We evaluate various compilation techniques for RPQs on different synthetic graph workloads.

Runtime verification (RV) consists in dynamically verifying that the event traces generated by single runs of a system under scrutiny (SUS) are compliant with the formal specification of its expected properties. RML (Runtime Monitoring Language) is a simple but expressive Domain Specific Language for RV; its semantics is based on a trace calculus formalized by a deterministic rewriting system which drives the implementation of the interpreter of the monitors generated by the RML compiler from the specifications. While determinism of the trace calculus ensures better performances of the generated monitors, it makes the semantics of its operators less intuitive. In this paper we move a first step towards a compositional semantics of the RML trace calculus, by interpreting its basic operators as operations on sets of instantiated event traces and by proving that such an interpretation is equivalent to the operational semantics of the calculus., In Proceedings EXPRESS/SOS 2020, arXiv:2008.12414. Author extended version is arXiv:2008.06453

Invertibility is a fundamental concept in computer science, with various manifestations in software development (serializer/deserializer, parser/printer, redo/undo, compressor/decompressor, and so on). Full invertibility necessarily requires bijectivity, but the direct approach of composing bijective functions to develop invertible programs is too restrictive to be useful. In this paper, we take a different approach by focusing on partially-invertible functions—functions that become invertible if some of their arguments are fixed. The simplest example of such is addition, which becomes invertible when fixing one of the operands. More involved examples include entropy-based compression methods (e.g., Huffman coding), which carry the occurrence frequency of input symbols (in certain formats such as Huffman tree), and fixing this frequency information makes the compression methods invertible. We develop a language Sparcl for programming such functions in a natural way, where partial-invertibility is the norm and bijectivity is a special case, hence gaining significant expressiveness without compromising correctness. The challenge in designing such a language is to allow ordinary programming (the “partially” part) to interact with the invertible part freely, and yet guarantee invertibility by construction. The language Sparcl is linear-typed, and has a type constructor to distinguish data that are subject to invertible computation and those that are not. We present the syntax, type system, and semantics of the language, and prove that Sparcl correctly guarantees invertibility for its programs. We demonstrate the expressiveness of Sparcl with examples including tree rebuilding from preorder and inorder traversals and Huffman coding.

While many cluster and grid computing frameworks are available, the task of building secure distributed systems or implementing distributed algorithms continue to be a challenging task due to the inherent distributed nature of such systems with multiple failure modes and security issues. In this paper, we present the design and development of remote method delegation (RMD), which is a secure lightweight grid computing platform with load balancing and code migration. RMD is focused on improving the usability issues that plague related industry solutions. The platform is implemented on the JVM (Java Virtual Machine) and supports the Java and Kotlin programming languages, however, the platform should theoretically work with other JVM languages. RMD was designed to simplify the implementation of distributed algorithms by providing a Kotlin DSL (domain specific language) that allows the programmer to define jobs within dedicated code blocks. Users from around the world can donate their own computing resources by hosting their own job server. RMD is secured by placing all untrusted code within a sandbox environment that prevents potentially malicious actions from taking place. To demonstrate the feasibility of the proposed model, a proof of concept implementation has been constructed with real examples demonstrating the usefulness of the proposed solution.

The construction of models of system behavior is of great importance throughout science and engineering. In bioengineering and bionanotechnology, these often take the form of dynamic models that specify the evolution of different species over time. To ensure that scientific observations and conclusions are consistent and that systems can be reliably engineered on the basis of model predictions, it is important that models of biomolecular systems can be constructed in a reliable, principled, and efficient manner. This review focuses on efforts to address this need by using domain-specific programming languages as the basis for custom design tools for researchers working on computational nucleic acid devices, where a domain-specific language is simply a programming language tailored to a particular application domain. The underlying thesis of our review is that there is a continuum of practical implementation strategies for computational nucleic acid systems, which can all benefit from appropriate domain-specific languages and software design tools. We emphasize the need for specialized yet flexible tools that can be realized using domain-specific languages that compile to more general-purpose representations.

This paper describes the history of the Groovy programming language. At the time of Groovy’s inception, Java was a dominant programming language with a wealth of useful libraries. Despite this, it was perceived by some to be evolving slowing and to have shortcomings for scripting, rapid prototyping and when trying to write minimalistic code. Other languages seemed to be innovating faster than Java and, while overcoming some of Java’s shortcomings, used syntax that was less familiar to Java developers. Integration with Java libraries was also non-optimal. Groovy was created as a complementary language to Java—its dynamic counterpart. It would look and feel like Java but focus on extensibility and rapid innovation. Groovy would borrow ideas from dynamic languages like Ruby, Python and Smalltalk where needed to provide compelling JVM solutions for some of Java’s shortcomings. Groovy supported innovation through its runtime and compile-time metaprogramming capabilities. It supported simple operator overloading, had a flexible grammar and was extensible. These characteristics made it suitable for growing the language to have new commands (verbs) and properties (nouns) specific to a particular domain, a so called Domain Specific Language (DSL). While still intrinsically linked with Java, over time Groovy has evolved from a niche dynamic scripting language into a compelling mainstream language. After many years as a principally dynamically-typed language, a static nature was added to Groovy. Code could be statically type checked or when dynamic features weren’t needed, they could be turned off entirely for Java-like performance. A number of nuances to the static nature came about to support the style of coding used by Groovy developers. Many choices made by Groovy in its design, later appeared in other languages (Swift, C#, Kotlin, Ceylon, PHP, Ruby, Coffeescript, Scala, Frege, TypeScript and Java itself). This includes Groovy’s dangling closure, Groovy builders, null-safe navigation, the Elvis operator, ranges, the spaceship operator, and flow typing. For most languages, we don’t know to what extent Groovy played a part in their choices. We do know that Kotlin took inspiration from Groovy’s dangling closures, builder concept, default it parameter for closures, templates and interpolated strings, null-safe navigation and the Elvis operator. The leadership, governance and sponsorship arrangements of Groovy have evolved over time, but Groovy has always been a successful highly collaborative open source project driven more by the needs of the community than by a vision of a particular company or person.

Logic programming languages such as Datalog have become popular as Domain Specific Languages (DSLs) for solving large-scale, real-world problems, in particular, static program analysis and network analysis. The logic specifications that model analysis problems process millions of tuples of data and contain hundreds of highly recursive rules. As a result, they are notoriously difficult to debug. While the database community has proposed several data provenance techniques that address the Declarative Debugging Challenge for Databases, in the cases of analysis problems, these state-of-the-art techniques do not scale. In this article, we introduce a novel bottom-up Datalog evaluation strategy for debugging: Our provenance evaluation strategy relies on a new provenance lattice that includes proof annotations and a new fixed-point semantics for semi-naïve evaluation. A debugging query mechanism allows arbitrary provenance queries, constructing partial proof trees of tuples with minimal height. We integrate our technique into Soufflé, a Datalog engine that synthesizes C++ code, and achieve high performance by using specialized parallel data structures. Experiments are conducted with D OOP /DaCapo, producing proof annotations for tens of millions of output tuples. We show that our method has a runtime overhead of 1.31× on average while being more flexible than existing state-of-the-art techniques.

Graphical PCB design tools like KiCAD lack support for high-level abstraction such as functions and loops. To improve PCB design productivity, we hereby present BHDL, a programming framework for PCB designs. In its compact and declarative syntax, schematics and layouts can be modeled effectively and expressed concisely. Treating all circuits, even a resistor, as functions, BHDL naturally supports modularized development that builds a complex design up from smaller designs hierarchically. As an embedded Domain Specific Language (eDSL), BHDL allows users to leverage the full feature of the host language for customization and extension. Our Jupyter kernel supports web-based, REPL-style development and generates auto-placed PCBs.

Smart environments are currently overpowering traditional Human-Computer Interaction (HCI) approaches for people's everyday life applications. Proxemic interaction is an emerging area for improving HCI experiences in such environments, causing the so-called proxemic environments to emerge. Proxemic interaction describes how the five dimensions (i.e., Distance, Identity, Location, Movement, and Orientation - DILMO) can be used to define interactions among people and digital devices. Current studies in this area are focused on developing proxemic applications with specific functionalities, based on a toolkit that allows developers to obtain DILMO information from sensors. However, there exists a notable lack of general approaches able to support the whole implementation process, starting from the modelling of proxemic environments to represent general proxemics behaviours, and finalizing with the development of specific applications. In order to facilitate the integration of proxemic capabilities in HCI, we propose an approach for modelling proxemic environments based on a graphical Domain-Specific Language (DSL). The DSL allows non-specialist designers to express proxemic interactions for modelling proxemic environments and supports the development process. In order to show its suitability and functionality, we have implemented a prototype to design proxemic behaviours in different proxemic environments.

At Philips IGT, we develop and produce interventional X-ray systems. For a controller in these systems, we have an approximately five years old domain specific language. Like general programming languages, domains specific languages also evolve. These languages co-evolve together with their domain. The language used at IGT was initially created for one system instance. Because of our positive experiences with the language, we want to evolve the language to support a family of systems. In this paper, we report on our experiences with the modifications we made to the original language. We made these changes preserving the behavior of the existing system instance. To prevent confidentiality issues, we use a Lego robot in our examples.

We present PrintTalk, a DSL to "program" 3D objects, called "gadgets". PrintTalk also features "topologies", which are predefined spacial arrangements of gadgets. Gadgets are composed by executing a gadget script (possibly consisting of subscripts) that 'draws' the gadget in the 3D scene. However, executing the script also returns a number of constraint variables. These variables can be constrained inside the gadget and can also be bound outside the gadget in order to constrain the produced gadgets after the facts. This is the essence of the gadget composition mechanism of PrintTalk. PrintTalk is implemented in DrRacket. Running a PrintTalk program generates a file that is sent to the 3D printer. We validate PrintTalk qualitatively by comparing the code for complex gadgets with the code needed to print those gadgets in existing languages.

International audience; Runtime monitoring and logging are fundamental techniques for analyzing and supervising the behavior of computer programs. However, supporting these techniques for a given language induces significant development costs that can hold language engineers back from providing adequate logging and monitoring tooling for new domain-specific modeling languages. Moreover, runtime monitoring and logging are generally considered as two different techniques: they are thus implemented separately which makes users prone to overlooking their potentially beneficial mutual interactions. We propose a language-agnostic, unifying framework for runtime monitoring and logging and demonstrate how it can be used to define loggers, runtime monitors and combinations of the two, aka. moniloggers. We provide an implementation of the framework that can be used with Java-based executable languages, and evaluate it on 2 implementations of the NabLab interpreter, leveraging in turn the instrumentation facilities offered by Truffle, and those offered by AspectJ.

We are witnessing an increasing interest in building recommender systems (RSs) for all sorts of Software Engineering activities. Modelling is no exception to this trend, as modelling environments are being enriched with RSs that help building models by providing recommendations based on previous solutions to similar problems in the same domain. However, building a RS from scratch requires considerable effort and specialized knowledge. To alleviate this problem, we propose an automated approach to the generation of RSs for modelling languages. Our approach is model-based, and we provide a domain-specific language called Droid to configure every aspect of the RS (like the type and features of the recommended items, the recommendation method, and the evaluation metrics). The RS so configured can be deployed as a service, and we offer out-of-the-box integration of this service with the EMF tree editor. To assess the usefulness of our proposal, we present a case study on the integration of a generated RS with a modelling chatbot, and report on an offline experiment measuring the precision and completeness of the recommendations, This project has received funding from the EU Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 813884, the Spanish Ministry of Science (RTI2018-095255-B-I00) and the R&D programme of Madrid (P2018/TCS-4314)

Two crucial aspects for the trustworthy utilization of domain-specific languages (DSLs) are their semantic correctness, and proper testing support for their users. Testing is frequently used to verify correctness, but is often done informally -- which may yield unreliable results -- and requires substantial effort for creating suitable test cases and oracles. To alleviate this situation, we propose an automated technique for building metamorphic testing environments for DSLs. Metamorphic testing identifies expected relationships between the outputs of two consecutive tests, reducing the effort in specifying oracles and creating test cases manually. This new ideas paper presents the overarching concepts, the architecture and a prototype implementation. We illustrate our proposal using a DSL to model and simulate data centres.

The language-oriented approach is becoming more and more popular in the development of information systems, but the existing DSM platforms that implement this paradigm have significant limitations, including insufficient expressive capabilities of the models used to implement visual model editors for complex subject areas and limited abilities to transform visual models. Visual languages are usually based on graph models, but the types of graphs used have certain limitations, such as insufficient expressiveness, the complexity of representing large-dimensional models and operation executions. For creating a tool that does not have the described constraints, development of a new formal model is needed. HP -graphs can become a solution for this problem. It is not only possible to create new visual languages for diverse domains based on them, but also to develop efficient algorithms to perform different operations on models constructed using these languages. The HP -graph definition is given and the justification of the expressive power of the proposed model is presented, the main operations for HP-graphs are described. The chosen graph formalism combines the capabilities of different types of graphs to represent visual models and allows creating a flexible model editor for the DSM platform, to implement effective algorithms of performing operations, in particular, model transformations.

A recent approach to tackle the ever increasing complexity of simulation system is model-driven engineering (MDE). However, it is mostly used to produce simulation tools, and seldom can perform formal analysis. Consequently, this raises issues like poor qualities of product, and falls short of non-functional requirements such as extensibility, maintainability, and reuse. In general, many of the success of MDE projects depend on the descriptive power of modeling languages and how conceptual models are implemented. Hence, this paper presents contributions in two main aspects: customizing domain specific language by metamodeling and enhancing model continuity by formalizing model transformations. A military application is used as a motivating example to illustrate the whole process by transforming the conceptual models into other more precise formalisms until they reach final executable models.

Currently, we have around us many Smart Objects. With the use of these objects, we can obtain benefits in our daily lives, as well as recommendations and help when we travel. Alternatively, we may increase and improve our industrial processes through the automation of certain tasks. Notwithstanding, we need to use specific software or to develop our own applications. Nevertheless, the main problem arises when we need to develop our own application because we need to save money, or in other cases, the existing applications are not adapted to us. In this case, it is possible that we need to learn new things, the money will then be spent, and such a process is likely to involve problems related to the Software Crisis. So, the main motivation is to create an environment which can reuse the previous knowledge and help people without knowledge about programming to create Smart Objects. Then, the research question of this paper is the following: Could we enable the creation of Smart Objects in an easy and efficient way for people who do not have programming skills? As a possible solution, we have developed a graphic Domain-Specific Language using the Midgar platform. In order to validate our proposal, we make an evaluation split into different phases; the first one consisted in measuring data obtained from participants when they were performing a specific task, and the second one consisted of a survey to collect their opinions about our proposal. Moreover, we also did a comparison of the measured data between two graphical editors and two different participant profiles according to their knowledge about Smart Objects.

Chatbot (and voicebot) applications are increasingly adopted in various domains such as e-commerce or customer services as a direct communication channel between companies and end-users. Multiple frameworks have been developed to ease their definition and deployment. While these frameworks are efficient to design simple chatbot applications, they still require advanced technical knowledge to define complex interactions and are difficult to evolve along with the company needs (e.g. it is typically impossible to change the NL engine provider). In addition, the deployment of a chatbot application usually requires a deep understanding of the targeted platforms, especially back-end connections, increasing the development and maintenance costs. In this paper, we introduce the Xatkit framework. Xatkit tackles these issues by providing a set of Domain Specific Languages to define chatbots (and voicebots and bots in general) in a platform-independent way. Xatkit also comes with a runtime engine that automatically deploys the chatbot application and manages the defined conversation logic over the platforms of choice. Xatkit's modular architecture facilitates the separate evolution of any of its components. Xatkit is open source and fully available online.

The problem of software language extension and composition drives much of the research in Software Language Engineering (SLE). Although various solutions have already been proposed, there is still little understanding of the specific ins and outs of this problem, which hinders the comparison and evaluation of existing solutionsIn this SoSyM Expert Voice, we introduce the Language Extension Problem as a way to better qualify the scope of the challenges related to language extension and compositionThe formulation of the problem is similar to the seminal Expression Problem introduced by Wadler in the late 1990s and lifts it from the extensibility of single constructs to the extensibility of groups of constructs, i.e., software languages. We provide a comprehensive definition of the actual constraints when considering language extension and believe the Language Extension Problem will drive future research in SLE, the same way the original Expression Problem helped to understand the strengths and weaknesses of programming languages and drove much research in programming languages.

Cloud computing offers a flexible pay-as-you-go model for provisioning application resources, which enables applications to scale on-demand based on the current workload. In many cases, though, users face the single vendor lock-in effect, missing opportunities for optimal and adaptive application deployment across multiple clouds. Several cloud modelling languages have been developed to support multi-cloud resource management, but still they lack holistic cloud management of all aspects and phases. This work defines the Cloud Application Modelling and Execution Language (CAMEL), which (i) allows users to specify the full set of design time aspects for multi-cloud applications, and (ii) supports the models@runtime paradigm that enables capturing an application’s current state facilitating its adaptive provisioning. CAMEL has been already used in many projects, domains and use cases due to its wide coverage of cloud management features. Finally, CAMEL has been positively evaluated in this work in terms of its usability and applicability in several domains (e.g., data farming, flight scheduling, financial services) based on the technology acceptance model (TAM).

In order to define executable hardware description language while at the same time be fit for formal proofs of properties, a hardware description language VeriFormal, embedded in Isabelle/HOL, was created. VeriFormal, together with a translator and Isabelle/HOL proof facility, provides a platform for designing, simulating and reasoning about hardware designs. Building such an environment is challenging due to the fact that the designer must have expertise in programming language design, the specific domain and theorem prover. It requires selection of a language design criteria, host language, grammar, embedding approach and techniques and mechanisms to address determinism and termination issues. When the language in question is a hardware description language, it requires specialized treatment of events, their scheduling, data types and assignments. In this paper, we report on our experience of embedding hardware description language VeriFormal in theorem prover Isabelle/HOL. In particular, the structure and execution of programs in the context of theorem provers and their impact on the overall language design are discussed. Among the main features of VeriFormal include formal semantics of the language, support for mechanical reasoning about designs and compiler and type checking of modules using Isabelle/HOL as well as VeriFormal type checkers.

The rise of programmable open distributed consensus platforms based on the blockchain technology has aroused a lot of interest in replicated stateful computations, aka smart contracts. As blockchains are used predominantly in financial applications, smart contracts frequently manage millions of dollars worth of virtual coins. Since smart contracts cannot be updated once deployed, the ability to reason about their correctness becomes a critical task. Yet, the de facto implementation standard, pioneered by the Ethereum platform, dictates smart contracts to be deployed in a low-level language, which renders independent audit and formal verification of deployed code infeasible in practice. We report an ongoing experiment held with an industrial blockchain vendor on designing, evaluating, and deploying Scilla, a new programming language for safe smart contracts. Scilla is positioned as an intermediate-level language, suitable to serve as a compilation target and also as an independent programming framework. Taking System F as a foundational calculus, Scilla offers strong safety guarantees by means of type soundness. It provides a clean separation between pure computational, state-manipulating, and communication aspects of smart contracts, avoiding many known pitfalls due to execution in a byzantine environment. We describe the motivation, design principles, and semantics of Scilla, and we report on Scilla use cases provided by the developer community. Finally, we present a framework for lightweight verification of Scilla programs, and showcase it with two domain-specific analyses on a suite of real-world use cases.

This paper proposes a fluent API generator for Scala, Haskell, and C++. It receives a grammar definition and generates a code skeleton of the library in the host programming language. The generated library is accessed through a chain of method calls; this style of API is called a fluent API. The library uses the host-language type checker to detect an invalid chain of method calls. Each method call is regarded as a lexical token in the embedded domain specific language implemented by that library. A sequence of the lexical tokens is checked and, if the sequence is not acceptable by the grammar, a type error is reported during compilation time. A contribution of this paper is to present an algorithm for generating the code-skeleton for a fluent API that reports a type error when a chain of method calls to the library does not match the given LR grammar. Our algorithm works in Scala, Haskell, and C++. To encode LR parsing, it uses the method/function overloading available in those languages. It does not need an advanced type system, or exponential compilation time or memory consumption. This paper also presents our implementation of the proposed generator.

The scope and scale of biological data are increasing at an exponential rate, as technologies like next-generation sequencing are becoming radically cheaper and more prevalent. Over the last two decades, the cost of sequencing a genome has dropped from $100 million to nearly $100—a factor of over 10 6 —and the amount of data to be analyzed has increased proportionally. Yet, as Moore’s Law continues to slow, computational biologists can no longer rely on computing hardware to compensate for the ever-increasing size of biological datasets. In a field where many researchers are primarily focused on biological analysis over computational optimization, the unfortunate solution to this problem is often to simply buy larger and faster machines. Here, we introduce Seq , the first language tailored specifically to bioinformatics, which marries the ease and productivity of Python with C-like performance. Seq starts with a subset of Python—and is in many cases a drop-in replacement—yet also incorporates novel bioinformatics- and computational genomics-oriented data types, language constructs and optimizations. Seq enables users to write high-level, Pythonic code without having to worry about low-level or domain-specific optimizations, and allows for the seamless expression of the algorithms, idioms and patterns found in many genomics or bioinformatics applications. We evaluated Seq on several standard computational genomics tasks like reverse complementation, k -mer manipulation, sequence pattern matching and large genomic index queries. On equivalent CPython code, Seq attains a performance improvement of up to two orders of magnitude, and a 160× improvement once domain-specific language features and optimizations are used. With parallelism, we demonstrate up to a 650× improvement. Compared to optimized C++ code, which is already difficult for most biologists to produce, Seq frequently attains up to a 2× improvement, and with shorter, cleaner code. Thus, Seq opens the door to an age of democratization of highly-optimized bioinformatics software.

An Intermediate Representation (IR) used by compilers is normally generated statically , as a result of parsing or analyzing the source program. This paper proposes a completely different type of IR, generated as a result of running the source program, the output-based IR . There is a practical translation problem where such an IR is useful, in the domain of test-pattern programs . Test-pattern programs run on ATE (automatic test equipment), a special embedded system to test semiconductors such as DRAMs. They generate a pattern for each clock, a bit vector input to the pins of the chip. One issue is that different ATEs require different programming since each ATE manufacturer has its own programming language. Nonetheless, we should be able to test a memory chip on different ATEs as long as they generate the same patterns with the same speed. Therefore, a memory chipmaker wants to make a pattern program portable across ATEs, to fully utilize their ATE resources. One solution is translating between pattern programs, for which we need an IR since there are multiple source ATEs and target ATEs. Instead of a conventional, static IR, we propose using the output pattern itself as an IR. Since the pattern is independent of ATEs and easily obtainable, the output-based IR obviates designing a static IR considering all ATE programming languages and hardware differences. Moreover, we might synthesize a better target program from the IR, more optimized to the target ATE. However, the full pattern generated by a product-level pattern program is huge, so we propose using an IR of abbreviated patterns, annotated with the repetition information obtained while executing the source program. Our experimental results with product-level pattern programs show that our approach is feasible.

Conducting technology-oriented experiments (i.e., experiments in which treatments are applied to objects by a computer-based tool) without proper tool support is often a time-consuming and highly error-prone task. Although many techniques have been proposed to help conducting controlled experiments, none of them simultaneously addresses (1) the executable specification of experiments at a high level of abstraction; (2) automated treatment execution and automated data analysis from the experiment specification; and (3) formal guaranties of the correctness of results according to an experiment specification for technology-oriented experiments. To address these issues, we provide a Domain-Specific Modeling approach to create a Web-based tool (ExpRunA ) comprising a Domain-Specific Language named ToExpDSL , execution and analysis script generators, a supporting framework, and a running infrastructure. An experimenter uses ToExpDSL to specify an experiment using experimentation concepts. From this specification, applications corresponding to the underlying treatments are executed, execution results are collected and analyzed, and, finally, the analysis results are presented to the experimenter. We establish the consistency of such results with respect to the experiment specification by formalizing and proving key correctness properties of ExpRunA . We empirically evaluated ExpRunA with respect to automation by replicating three already published experiments; we evaluated the level of abstraction by a qualitative assessment. Our empirical evaluation shows that ToExpDSL is expressive enough to specify three technology-oriented experiments and that ExpRunA can be used to enable sound automation of execution and analysis from the specification of technology-oriented experiments at a high level of abstraction.

Code optimization is an intricate task that is getting more complex as computing systems evolve. Managing the program optimization process, including the implementation and evaluation of code variants, is tedious, inefficient, and errors are likely to be introduced in the process. Moreover, because each platform typically requires a different sequence of transformations to fully harness its computing power, the optimization process complexity grows as new platforms are adopted. To address these issues, systems and frameworks have been proposed to automate the code optimization process. They, however, have not been widely adopted and are primarily used by experts with deep knowledge about underlying architecture and compiler intricacies. This article describes the requirements that we believe necessary for making automatic performance tuning more broadly used, especially in complex, long-lived high-performance computing applications. Besides discussing limitations of current systems and strategies to overcome these, we describe the design of a system that is able to semi-automatically generate efficient platform-specific code. In the proposed system, the code optimization is programmer-guided, separately from application code, on an external file in what we call optimization programming. The language to program the optimization process is able to represent complex collections of transformations and, as a result, generate efficient platform-specific code. A database manages different optimized versions of code regions, providing a pragmatic approach to performance portability, and the framework itself has separate components, allowing the optimized code to be used on systems without installing all of the modules required for the code generation. We present experiments on two different platforms to illustrate the generation of efficient platform-specific code that performs comparable to hand-optimized, vendor-provided code.

Background: Creating a scalable computational infrastructure to analyze the wealth of information contained in data repositories is difficult due to significant barriers in organizing, extracting and analyzing relevant data. Shared data science infrastructures like Boa_g is needed to efficiently process and parse data contained in large data repositories. The main features of Boa_g are inspired from existing languages for data intensive computing and can easily integrate data from biological data repositories. Results: As a proof of concept, Boa for genomics, Boa_g, has been implemented to analyze RefSeq’s 153,848 annotation (GFF) and assembly (FASTA) file metadata. Boa_g provides a massive improvement from existing solutions like Python and MongoDB, by utilizing a domain-specific language that uses Hadoop infrastructure for a smaller storage footprint that scales well and requires fewer lines of code. We execute scripts through Boa_g to answer questions about the genomes in RefSeq. We identify the largest and smallest genomes deposited, explore exon frequencies for assemblies after 2016, identify the most commonly used bacterial genome assembly program, and address how animal genome assemblies have improved since 2016. Boa_g databases provide a significant reduction in required storage of the raw data and a significant speed up in its ability to query large datasets due to automated parallelization and distribution of Hadoop infrastructure during computations. Conclusions: In order to keep pace with our ability to produce biological data, innovative methods are required. The Shared Data Science Infrastructure, Boa_g, provides researchers a greater access to researchers to efficiently explore data in new ways. We demonstrate the potential of a the domain specific language Boa_g using the RefSeq database to explore how deposited genome assemblies and annotations are changing over time. This is a small example of how Boa_g could be used with large biological datasets.

An increasing attention has been given to provide service level objectives (SLOs) in stream processing applications due to the performance and energy requirements, and because of the need to impose limits in terms of resource usage while improving the system utilization. Since the current and next-generation computing systems are intrinsically offering parallel architectures, the software has to naturally exploit the architecture parallelism. Implement and meet SLOs on existing applications is not a trivial task for application programmers, since the software development process, besides the parallelism exploitation, requires the implementation of autonomic algorithms or strategies. This is a system-oriented programming approach and requires the management of multiple knobs and sensors (e.g., the number of threads to use, the clock frequency of the cores, etc.) so that the system can self-adapt at runtime. In this work, we introduce a new and simpler way to define SLO in the application’s source code, by abstracting from the programmer all the details relative to self-adaptive system implementation. The application programmer specifies which parts of the code to parallelize and the related SLOs that should be enforced. To reach this goal, source-to-source code transformation rules are implemented in our compiler, which automatically generates self-adaptive strategies to enforce, at runtime, the user-expressed objectives. The experiments highlighted promising results with simpler, effective, and efficient SLO implementations for real-world applications.

The use of code quality control platforms for analysing source code is increasingly gaining attention in the developer community. These platforms are prepared to parse and check source code written in a variety of general-purpose programming languages. The emergence of domain-specific languages enables professionals from different areas to develop and describe problem solutions in their disciplines. Thus, source code quality analysis methods and tools can also be applied to software artefacts developed with a domain-specific language. To evaluate the quality of domain-specific language code, every software component required by the quality platform to parse and query the source code must be developed. This becomes a time-consuming and error-prone task, for which this paper describes a model-driven interoperability strategy that bridges the gap between the grammar formats of source code quality parsers and domain-specific text languages. This approach has been tested on the most widespread platforms for designing text-based languages and source code analysis. This interoperability approach has been evaluated on a number of specific contexts in different domain areas.

Wider adoption of model-driven engineering leads to an abundance of models and metamodels in academic and industrial practice. One of the key techniques for the management and maintenance of such artifacts is model clone detection, where highly similar (meta-)models and (meta-)model fragments are mined from a typically large set of data. In this paper we have extended the SAMOS framework (Statistical Analysis of MOdelS) to clone detection, exemplified on Ecore metamodels. Our clone detection approach uses and extends the framework’s feature extraction, vector space model, natural language processing and clustering capabilities. We performed three extensive case studies to demonstrate its accuracy both quantitatively and qualitatively. We first compared the sensitivity and accuracy of SAMOS for metamodel changes through mutation and scenario analysis (which simulate clones) with those of NICAD-Ecore and MACH, tools for clone detection on Ecore and UML models respectively. We then compared the precision and recall of SAMOS and of NICAD-Ecore on a real dataset, consisting of conference management metamodels from the ATL Zoo. Finally we performed a repository-wide mining of metamodel clones from GitHub. We conclude that SAMOS stands out with its higher accuracy and yet considerable scalability for further large-scale clone detection and other empirical studies on metamodels and domain specific languages.

As software permeates our lives, bugs become increasingly expensive; the best way to reduce their cost is to reduce the number of bugs. Of course, this is easier said than done and, at best, we can go after their root causes to mitigate them. One of such causes is state, whether it is the state of a light bulb (i.e. on/off), or the state of a complex protocol, reasoning about state is a complex process which developers are required to do with subpar tools. Ideally, we want to specify constraints and have the computer reason for us; typestates enable developers to describe states using the type system and allow the compiler to reason about them. We propose an approach to bring typestates to Rust, without any external tools, leveraging only Rust's type and macro systems. Our approach provides a macro-based domain-specific language which enables developers to easily express and implement typestates, along with certain state machine safety guarantees, it is open-source and available at https://github.com/rustype/typestate-rs. publishersversion published

This article introduces an intelligent assistant architecture that allows replacing existing modules and plugging new modules without requiring any code changes thanks to its modular and extensible structure. Our proposal supports multiple natural languages and at the same time allows the integration of any external artificial intelligence or IoT component easily. The software that we developed understands the commands in English and Turkish languages and transmits the answers to the user in these languages. Through our domain-specific language, different artificial intelligence components can be integrated into the system. Thanks to this domain specific language, only specific conversations are detected and the artificial intelligence module can be called. However, human speech contains many synonyms and we cannot always achieve the exact word by word. For this reason, we offer a system that understands the meanings of sentences and learns what the user wants to do when necessary.