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This report documents the program and the outcomes of Dagstuhl Seminar 22362 "Model-Driven Engineering of Digital Twins". Digital twins are an emerging concept with the potential for revolutionising the way we interact with the physical world. Digital twins can be used for improved analysis and understanding of complex systems as well as for control and transformation of these systems. Digital twins are themselves complex software systems, posing novel software-engineering challenges, which have so far not been sufficiently addressed by the software-engineering research community. The seminar aimed as a key outcome to contribute to a solid research roadmap for the new Software Engineering subdiscipline of Model-Based Development of Digital Twins. This paper is an intermediate result, which is thought to be further discussed in the research community that has also been built using this seminar.

Obtaining a relevant dataset is central to conducting empirical studies in software engineering. However, in the context of mining software repositories, the lack of appropriate tooling for large scale mining tasks hinders the creation of new datasets. Moreover, limitations related to data sources that change over time (e.g., code bases) and the lack of documentation of extraction processes make it difficult to reproduce datasets over time. This threatens the quality and reproducibility of empirical studies. In this paper, we propose a tool-supported approach facilitating the creation of large tailored datasets while ensuring their reproducibility. We leveraged all the sources feeding the Software Heritage append-only archive which are accessible through a unified programming interface to outline a reproducible and generic extraction process. We propose a way to define a unique fingerprint to characterize a dataset which, when provided to the extraction process, ensures that the same dataset will be extracted. We demonstrate the feasibility of our approach by implementing a prototype. We show how it can help reduce the limitations researchers face when creating or reproducing datasets.

Exploratory programming is a software development style in which code is a medium for prototyping ideas and solutions, and in which even the end-goal can evolve over time. Exploratory programming is valuable in various contexts such as programming education, data science, and end-user programming. However, there is a lack of appropriate tooling and language design principles to support exploratory programming. This paper presents a host language- and object language-independent protocol for exploratory programming akin to the Language Server Protocol. The protocol serves as a basis to develop novel (or extend existing) programming environments for exploratory programming such as computational notebooks and command-line REPLs. An architecture is presented on top of which prototype environments can be developed with relative ease, because existing (language) components can be reused. Our prototypes demonstrate that the proposed protocol is sufficiently expressive to support exploratory programming scenarios as encountered in literature within the software engineering, human-computer interaction and data science domains.

Properly arranging models, data sources, and their relations to engineer digital twins is challenging. We propose a conceptual modeling framework for digital twins that captures the combined usage of heterogeneous models and their respective evolving data for the twinâs entire lifecycle.

Language models are promising solutions for tackling increasing complex problems. In software engineering, they recently attracted attention in code assistants, with programs automatically written in a given programming language from a programming task description in natural language. They have the potential to save time and effort when writing code. However, these systems are currently poorly understood, preventing them from being used optimally. In this paper, we investigate the various input parameters of two language models, and conduct a study to understand if variations of these input parameters (e.g. programming task description and the surrounding context, creativity of the language model, number of generated solutions) can have a significant impact on the quality of the generated programs. We design specific operators for varying input parameters and apply them over two code assistants (Copilot and Codex) and two benchmarks representing algorithmic problems (HumanEval and LeetCode). Our results showed that varying the input parameters can significantly improve the performance of language models. However, there is a tight dependency when varying the temperature, the prompt and the number of generated solutions, making potentially hard for developers to properly control the parameters to obtain an optimal result. This work opens opportunities to propose (automated) strategies for improving performance., Comment: 14 pages, 3 Figures (not counted the subfigures), 10 Tables

Numerous software systems are highly configurable through run-time options, such as command-line parameters. Users can tune some of the options to meet various functional and non-functional requirements such as footprint, security, or execution time. However, some options are never set for a given system instance, and their values remain the same whatever the use cases of the system. Herein, we design a controlled experiment in which the system's run-time configuration space can be specialized at compile-time and combinations of options can be removed on demand. We perform an in-depth study of the well-known x264 video encoder and quantify the effects of its specialization to its non-functional properties, namely on binary size, attack surface, and performance while ensuring its validity. Our exploratory study suggests that the configurable specialization of a system has statistically significant benefits on most of its analysed non-functional properties, which benefits depend on the number of the debloated options. While our empirical results and insights show the importance of removing code related to unused run-time options to improve software systems, an open challenge is to further automate the specialization process., Comment: 14 pages, 3 Figures (not counted the subfigures), 5 Tables, and 1 Algorithm

The General Data Protection Regulation (GDPR) forces IT companies to comply with a number of principles when dealing with European citizens' personal data. Non-compliant companies are exposed to penalties which may represent up to 4% of their turnover. Currently, it is very hard for companies driven by personal data to make their applications GDPR-compliant, especially if those applications were developed before the GDPR was established. We present rgpdOS, a GDPR-aware operating system that aims to bring GDPR-compliance to every application, while requiring minimal changes to application code.

The models and data framework demystifies the different roles that models and data play in software development and operation and clarifies where machine learning and artificial intelligence techniques could be used.

The models and data framework demystifies the different roles that models and data play in software development and operation and clarifies where machine learning and artificial intelligence techniques could be used.

The models and data framework demystifies the different roles that models and data play in software development and operation and clarifies where machine learning and artificial intelligence techniques could be used.

This chapter targets a better understanding of the compositionality of analyses, including different forms of compositionality and specific conditions of composition. Analysis involves models, contexts, and properties. These are all expressed in languages with their own semantics. For a successful composition of analyses, it is therefore important to compose models as well as the underlying languages. We aim to develop a better understanding of what is needed to answer questions such as “When I want to compose two or more analyses, what do I need to take into account?” We describe the elements impacting analysis compositionality, the relation of these elements to analysis, and how composition of analysis relates to compositionality of these elements. This core chapter addresses Challenge 1 introduced in Chap. 3 of this book (the theoretical foundations—how to compose the underlying languages, models, and analyses).

This chapter gives an introduction to the key concepts and terminology relevant for model-based analysis tools and their composition. In the first half of the chapter, we introduce concepts relevant for modelling and composition of models and modelling languages. The second half of the chapter then focuses on concepts relevant to analysis and analysis composition. This chapter, thus, lays the foundations for the remainder of the book, ensuring that readers can go through the book as a coherent piece.

The models and data framework demystifies the different roles that models and data play in software development and operation and clarifies where machine learning and artificial intelligence techniques could be used.

[EN] Modeling is requiring increasingly larger efforts while becoming indispensable given the complexity of the problems we are solving. Modelers face high cognitive load to understand a multitude of complex abstractions and their relationships. There is an urgent need to better support tool builders to ultimately provide modelers with intelligent modeling assistance that learns from previous modeling experiences, automatically derives modeling knowledge, and provides context-aware assistance. However, current intelligent modeling assistants (IMAs) lack adaptability and flexibility for tool builders, and do not facilitate understanding the differences and commonalities of IMAs for modelers. Such a patchwork of limited IMAs is a lost opportunity to provide modelers with better support for the creative and rigorous aspects of software engineering. In this expert voice, we present a conceptual reference framework (RF-IMA) and its properties to identify the foundations for intelligent modeling assistance. For tool builders, RF-IMA aims to help build IMAs more systematically. For modelers, RF-IMA aims to facilitate comprehension, comparison, and integration of IMAs, and ultimately to provide more intelligent support. We envision a momentum in the modeling community that leads to the implementation of RF-IMA and consequently future IMAs. We identify open challenges that need to be addressed to realize the opportunities provided by intelligent modeling assistance.

[EN] Modeling is requiring increasingly larger efforts while becoming indispensable given the complexity of the problems we are solving. Modelers face high cognitive load to understand a multitude of complex abstractions and their relationships. There is an urgent need to better support tool builders to ultimately provide modelers with intelligent modeling assistance that learns from previous modeling experiences, automatically derives modeling knowledge, and provides context-aware assistance. However, current intelligent modeling assistants (IMAs) lack adaptability and flexibility for tool builders, and do not facilitate understanding the differences and commonalities of IMAs for modelers. Such a patchwork of limited IMAs is a lost opportunity to provide modelers with better support for the creative and rigorous aspects of software engineering. In this expert voice, we present a conceptual reference framework (RF-IMA) and its properties to identify the foundations for intelligent modeling assistance. For tool builders, RF-IMA aims to help build IMAs more systematically. For modelers, RF-IMA aims to facilitate comprehension, comparison, and integration of IMAs, and ultimately to provide more intelligent support. We envision a momentum in the modeling community that leads to the implementation of RF-IMA and consequently future IMAs. We identify open challenges that need to be addressed to realize the opportunities provided by intelligent modeling assistance.

Sustainability has emerged as a concern of central relevance. As a wicked problem, it poses challenges to business-as-usual in many areas, including that of modeling. This article addresses a question at the intersection of model-driven engineering and sustainability research: How can we better support sustainability by bringing together model-driven engineering, data, visualization and self-adaptive systems, to facilitate engagement, exploration, and understanding of the effects that individual and organizational choices have on sustainability? We explore this question via an idealized vision of an evaluation environment that facilitates integration and mapping of models from multiple diverse sources, visual exploration, and evaluation of what-if scenarios, for stakeholders with divergent perspectives. The article identifies research challenges to be addressed to enable decision making to support sustainability and provides a map of sustainability modeling issues across disciplines.

Sustainability has emerged as a concern of central relevance. As a wicked problem, it poses challenges to business-as-usual in many areas, including that of modeling. This article addresses a question at the intersection of model-driven engineering and sustainability research: How can we better support sustainability by bringing together model-driven engineering, data, visualization and self-adaptive systems, to facilitate engagement, exploration, and understanding of the effects that individual and organizational choices have on sustainability? We explore this question via an idealized vision of an evaluation environment that facilitates integration and mapping of models from multiple diverse sources, visual exploration, and evaluation of what-if scenarios, for stakeholders with divergent perspectives. The article identifies research challenges to be addressed to enable decision making to support sustainability and provides a map of sustainability modeling issues across disciplines.

[EN] The ever-growing complexity of systems, the growing number of stakeholders, and the corresponding continuous emergence of new domain-specific modeling abstractions has led to significantly higher cognitive load on modelers. There is an urgent need to provide modelers with better, more Intelligent Modeling Assistants (IMAs). An important factor to consider is the ability to assess and compare, to learn from existing and inform future IMAs, while potentially combining them. Recently, a conceptual Reference Framework for Intelligent Modeling Assistance (RF-IMA) was proposed. RF-IMA defines the main required components and high-level properties of IMAs. In this paper, we present a detailed, level-wise definition for the properties of RF-IMA to enable a better understanding, comparison, and selection of existing and future IMAs. The proposed levels are a first step towards a comprehensive assessment grid for intelligent modeling assistance. For an initial validation of the proposed levels, we assess the existing landscape of intelligent modeling assistance and three future scenarios of intelligent modeling assistance against these levels.

[EN] Modeling is requiring increasingly larger efforts while becoming indispensable given the complexity of the problems we are solving. Modelers face high cognitive load to understand a multitude of complex abstractions and their relationships. There is an urgent need to better support tool builders to ultimately provide modelers with intelligent modeling assistance that learns from previous modeling experiences, automatically derives modeling knowledge, and provides context-aware assistance. However, current intelligent modeling assistants (IMAs) lack adaptability and flexibility for tool builders, and do not facilitate understanding the differences and commonalities of IMAs for modelers. Such a patchwork of limited IMAs is a lost opportunity to provide modelers with better support for the creative and rigorous aspects of software engineering. In this expert voice, we present a conceptual reference framework (RF-IMA) and its properties to identify the foundations for intelligent modeling assistance. For tool builders, RF-IMA aims to help build IMAs more systematically. For modelers, RF-IMA aims to facilitate comprehension, comparison, and integration of IMAs, and ultimately to provide more intelligent support. We envision a momentum in the modeling community that leads to the implementation of RF-IMA and consequently future IMAs. We identify open challenges that need to be addressed to realize the opportunities provided by intelligent modeling assistance.

[EN] The ever-growing complexity of systems, the growing number of stakeholders, and the corresponding continuous emergence of new domain-specific modeling abstractions has led to significantly higher cognitive load on modelers. There is an urgent need to provide modelers with better, more Intelligent Modeling Assistants (IMAs). An important factor to consider is the ability to assess and compare, to learn from existing and inform future IMAs, while potentially combining them. Recently, a conceptual Reference Framework for Intelligent Modeling Assistance (RF-IMA) was proposed. RF-IMA defines the main required components and high-level properties of IMAs. In this paper, we present a detailed, level-wise definition for the properties of RF-IMA to enable a better understanding, comparison, and selection of existing and future IMAs. The proposed levels are a first step towards a comprehensive assessment grid for intelligent modeling assistance. For an initial validation of the proposed levels, we assess the existing landscape of intelligent modeling assistance and three future scenarios of intelligent modeling assistance against these levels.

[EN] The ever-growing complexity of systems, the growing number of stakeholders, and the corresponding continuous emergence of new domain-specific modeling abstractions has led to significantly higher cognitive load on modelers. There is an urgent need to provide modelers with better, more Intelligent Modeling Assistants (IMAs). An important factor to consider is the ability to assess and compare, to learn from existing and inform future IMAs, while potentially combining them. Recently, a conceptual Reference Framework for Intelligent Modeling Assistance (RF-IMA) was proposed. RF-IMA defines the main required components and high-level properties of IMAs. In this paper, we present a detailed, level-wise definition for the properties of RF-IMA to enable a better understanding, comparison, and selection of existing and future IMAs. The proposed levels are a first step towards a comprehensive assessment grid for intelligent modeling assistance. For an initial validation of the proposed levels, we assess the existing landscape of intelligent modeling assistance and three future scenarios of intelligent modeling assistance against these levels.

Sustainability has emerged as a concern of central relevance. As a wicked problem, it poses challenges to business-as-usual in many areas, including that of modeling. This article addresses a question at the intersection of model-driven engineering and sustainability research: How can we better support sustainability by bringing together model-driven engineering, data, visualization and self-adaptive systems, to facilitate engagement, exploration, and understanding of the effects that individual and organizational choices have on sustainability? We explore this question via an idealized vision of an evaluation environment that facilitates integration and mapping of models from multiple diverse sources, visual exploration, and evaluation of what-if scenarios, for stakeholders with divergent perspectives. The article identifies research challenges to be addressed to enable decision making to support sustainability and provides a map of sustainability modeling issues across disciplines.

Sustainability has emerged as a concern of central relevance. As a wicked problem, it poses challenges to business-as-usual in many areas, including that of modeling. This article addresses a question at the intersection of model-driven engineering and sustainability research: How can we better support sustainability by bringing together model-driven engineering, data, visualization and self-adaptive systems, to facilitate engagement, exploration, and understanding of the effects that individual and organizational choices have on sustainability? We explore this question via an idealized vision of an evaluation environment that facilitates integration and mapping of models from multiple diverse sources, visual exploration, and evaluation of what-if scenarios, for stakeholders with divergent perspectives. The article identifies research challenges to be addressed to enable decision making to support sustainability and provides a map of sustainability modeling issues across disciplines.

This editorial presents the various forms of open access, discusses their pros and cons from the perspective of the Journal of Object Technology and its editors in chiefs, and illustrates how JOT implements a platinum open access model. The regular reader will also notice that this editorial features a new template for the journal that will be used from now on., Comment: 5 pages

We introduce Approximate Unrolling, a compiler loop optimization that reduces execution time and energy consumption, exploiting code regions that can endure some approximation and still produce acceptable results. Specifically, this work focuses on counted loops that map a function over the elements of an array. Approximate Unrolling transforms loops similarly to Loop Unrolling. However, unlike its exact counterpart, our optimization does not unroll loops by adding exact copies of the loop's body. Instead, it adds code that interpolates the results of previous iterations., QC 20190819

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 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 sixtimes 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., QC 20180323

The growing complexity of everyday life systems (and devices) over the last decades has forced the industry to use and investigate different development techniques to manage the many different aspects of the systems. In this context, the use of model driven engineering (MDE) has emerged and is now common practice for many engineering disciplines. However, this comes with important challenges. As set of main challenges relates to the fact that different modeling techniques, languages, and tools are required to deal with the different system aspects, and that support is required to ensure consistence and coherence between the different models. This paper identifies a number of the challenges and paints a roadmap on how tooling can support a multi-model integrated way of working.

QC 20220927

The growing complexity of everyday life systems (and devices) over the last decades has forced the industry to use and investigate different development techniques to manage the many different aspects of the systems. In this context, the use of model driven engineering (MDE) has emerged and is now common practice for many engineering disciplines. However, this comes with important challenges. As set of main challenges relates to the fact that different modeling techniques, languages, and tools are required to deal with the different system aspects, and that support is required to ensure consistence and coherence between the different models. This paper identifies a number of the challenges and paints a roadmap on how tooling can support a multi-model integrated way of working.

Software is often a critical component of scientific research. It can be a component of the academic research methods used to produce research results, or it may itself be an academic research result. Software, however, has rarely been considered to be a citable artifact in its own right. With the advent of open-source software, artifact evaluation committees of conferences, and journals that include source code and running systems as part of the published artifacts, we foresee that software will increasingly be recognized as part of the academic process. The quality and sustainability of this software must be accounted for, both a prioro and a posteriori. The Dagstuhl Perspectives Workshop on "Engineering Academic Software" has examined the strengths, weaknesses, risks, and opportunities of academic software engineering. A key outcome of the workshop is this Dagstuhl Manifesto, serving as a roadmap towards future professional software engineering for software-based research instruments and other software produced and used in an academic context. The manifesto is expressed in terms of a series of actionable "pledges" that users and developers of academic research software can take as concrete steps towards improving the environment in which that software is produced.

The use of domain-specific languages (DSLs) has become a successful technique to develop complex sys tems. In this context, an emerging phenomenon is the existence of DSL variants, which are different versions of a DSL adapted to specific purposes but that still share commonalities. In such a case, the challenge for language designers is to reuse, as much as possible, previously defined language constructs to narrow implementation from scratch. To overcome this challenge, recent research in software lan guages engineering introduced the notion of language product lines. Similarly to software product lines, language product lines are often built from a set of existing DSL variants. In this article, we propose a reverse-engineering technique to ease-off such a development scenario. Our approach receives a set of DSL variants which are used to automatically recover a language modular design and to synthesize the corresponding variability models. The validation is performed in a project involving industrial partners that required three different variants of a DSL for finite state machines. This validation shows that our approach is able to correctly identify commonalities and variability.

The use of domain-specific languages (DSLs) has become a successful technique to develop complex sys tems. In this context, an emerging phenomenon is the existence of DSL variants, which are different versions of a DSL adapted to specific purposes but that still share commonalities. In such a case, the challenge for language designers is to reuse, as much as possible, previously defined language constructs to narrow implementation from scratch. To overcome this challenge, recent research in software lan guages engineering introduced the notion of language product lines. Similarly to software product lines, language product lines are often built from a set of existing DSL variants. In this article, we propose a reverse-engineering technique to ease-off such a development scenario. Our approach receives a set of DSL variants which are used to automatically recover a language modular design and to synthesize the corresponding variability models. The validation is performed in a project involving industrial partners that required three different variants of a DSL for finite state machines. This validation shows that our approach is able to correctly identify commonalities and variability.

The use of domain-specific languages (DSLs) has become a successful technique to develop complex sys tems. In this context, an emerging phenomenon is the existence of DSL variants, which are different versions of a DSL adapted to specific purposes but that still share commonalities. In such a case, the challenge for language designers is to reuse, as much as possible, previously defined language constructs to narrow implementation from scratch. To overcome this challenge, recent research in software lan guages engineering introduced the notion of language product lines. Similarly to software product lines, language product lines are often built from a set of existing DSL variants. In this article, we propose a reverse-engineering technique to ease-off such a development scenario. Our approach receives a set of DSL variants which are used to automatically recover a language modular design and to synthesize the corresponding variability models. The validation is performed in a project involving industrial partners that required three different variants of a DSL for finite state machines. This validation shows that our approach is able to correctly identify commonalities and variability.

Software is often a critical component of scientific research. It can be a component of the academic research methods used to produce research results, or it may itself be an academic research result. Software, however, has rarely been considered to be a citable artifact in its own right. With the advent of open-source software, artifact evaluation committees of conferences, and journals that include source code and running systems as part of the published artifacts, we foresee that software will increasingly be recognized as part of the academic process. The quality and sustainability of this software must be accounted for, both a prioro and a posteriori. The Dagstuhl Perspectives Workshop on "Engineering Academic Software" has examined the strengths, weaknesses, risks, and opportunities of academic software engineering. A key outcome of the workshop is this Dagstuhl Manifesto, serving as a roadmap towards future professional software engineering for software-based research instruments and other software produced and used in an academic context. The manifesto is expressed in terms of a series of actionable "pledges" that users and developers of academic research software can take as concrete steps towards improving the environment in which that software is produced.

Many project-specific languages, including in particular filtering languages, are defined using non-formal specifications written in natural languages. This leads to ambiguities and errors in the specification of those languages. This paper reports on an industrial experiment on using a tool-supported language specification framework (K) for the formal specification of the syntax and semantics of a filtering language having a complexity similar to those of real-life projects. This experimentation aims at estimating, in a specific industrial setting, the difficulty and benefits of formally specifying a packet filtering language using a tool-supported formal approach., Comment: In Proceedings F-IDE 2016, arXiv:1701.07925

Many project-specific languages, including in particular filtering languages, are defined using nonformal specifications written in natural languages. This leads to ambiguities and errors in the specification of those languages. This paper reports on an industrial experiment on using a tool-supported language specification framework (K) for the formal specification of the syntax and semantics of a filtering language having a complexity similar to those of real-life projects. This experimentation aims at estimating, in a specific industrial setting, the difficulty and benefits of formally specifying a packet filtering language using a tool-supported formal approach.

The use of domain-specific languages (DSLs) is a successful technique in the development of complex systems. Indeed, the construction of new DSLs addressing the particular needs of software projects has become a recurrent activity. In this context, the phenomenon of specification cloning has started to appear. Language designers often copy&paste some parts of the specification from legacy DSLs to “reuse” formerly defined language constructs. As well known, this type of practices introduce problems such as bugs propagation, thus increasing of maintenance costs. In this paper, we present Puzzle, a tool that uses static analysis to facilitate the detection of specification clones in DSLs implemented under the executable metamodeling paradigm. Puzzle also enables the extraction specification clones as reusable language modules that can be later used to build up new DSLs.

The use of domain-specific languages (DSLs) has become a successful technique in the development of complex systems. Nevertheless, the construction of this type of languages is time-consuming and requires highly-specialized knowledge and skills. An emerging practice to facilitate this task is to enable reuse through the definition of lan-guage modules which can be later put together to build up new DSLs. Still, the identification and definition of language modules are complex and errorprone activities, thus hindering the reuse exploitation when developing DSLs. In this paper, we propose a computer-aided approach to (i) identify potential reuse in a set of legacy DSLs; and (ii) capitalize such potential reuse by extracting a set of reusable language modules with well defined interfaces that facilitate their assembly. We validate our approach by using realistic DSLs coming out from industrial case studies and obtained from public GitHub repositories.

The use of domain-specific languages (DSLs) is a successful technique in the development of complex systems. Indeed, the construction of new DSLs addressing the particular needs of software projects has become a recurrent activity. In this context, the phenomenon of specification cloning has started to appear. Language designers often copy&paste some parts of the specification from legacy DSLs to “reuse” formerly defined language constructs. As well known, this type of practices introduce problems such as bugs propagation, thus increasing of maintenance costs. In this paper, we present Puzzle, a tool that uses static analysis to facilitate the detection of specification clones in DSLs implemented under the executable metamodeling paradigm. Puzzle also enables the extraction specification clones as reusable language modules that can be later used to build up new DSLs.

Many project-specific languages, including in particular filtering languages, are defined using nonformal specifications written in natural languages. This leads to ambiguities and errors in the specification of those languages. This paper reports on an industrial experiment on using a tool-supported language specification framework (K) for the formal specification of the syntax and semantics of a filtering language having a complexity similar to those of real-life projects. This experimentation aims at estimating, in a specific industrial setting, the difficulty and benefits of formally specifying a packet filtering language using a tool-supported formal approach.

The use of domain-specific languages (DSLs) has become a successful technique in the development of complex systems. Nevertheless, the construction of this type of languages is time-consuming and requires highly-specialized knowledge and skills. An emerging practice to facilitate this task is to enable reuse through the definition of lan-guage modules which can be later put together to build up new DSLs. Still, the identification and definition of language modules are complex and errorprone activities, thus hindering the reuse exploitation when developing DSLs. In this paper, we propose a computer-aided approach to (i) identify potential reuse in a set of legacy DSLs; and (ii) capitalize such potential reuse by extracting a set of reusable language modules with well defined interfaces that facilitate their assembly. We validate our approach by using realistic DSLs coming out from industrial case studies and obtained from public GitHub repositories.

The use of domain-specific languages (DSLs) is a successful technique in the development of complex systems. Indeed, the construction of new DSLs addressing the particular needs of software projects has become a recurrent activity. In this context, the phenomenon of specification cloning has started to appear. Language designers often copy&paste some parts of the specification from legacy DSLs to “reuse” formerly defined language constructs. As well known, this type of practices introduce problems such as bugs propagation, thus increasing of maintenance costs. In this paper, we present Puzzle, a tool that uses static analysis to facilitate the detection of specification clones in DSLs implemented under the executable metamodeling paradigm. Puzzle also enables the extraction specification clones as reusable language modules that can be later used to build up new DSLs.

Many project-specific languages, including in particular filtering languages, are defined using nonformal specifications written in natural languages. This leads to ambiguities and errors in the specification of those languages. This paper reports on an industrial experiment on using a tool-supported language specification framework (K) for the formal specification of the syntax and semantics of a filtering language having a complexity similar to those of real-life projects. This experimentation aims at estimating, in a specific industrial setting, the difficulty and benefits of formally specifying a packet filtering language using a tool-supported formal approach.

The use of domain-specific languages (DSLs) is a successful technique in the development of complex systems. Indeed, the construction of new DSLs addressing the particular needs of software projects has become a recurrent activity. In this context, the phenomenon of specification cloning has started to appear. Language designers often copy&paste some parts of the specification from legacy DSLs to “reuse” formerly defined language constructs. As well known, this type of practices introduce problems such as bugs propagation, thus increasing of maintenance costs. In this paper, we present Puzzle, a tool that uses static analysis to facilitate the detection of specification clones in DSLs implemented under the executable metamodeling paradigm. Puzzle also enables the extraction specification clones as reusable language modules that can be later used to build up new DSLs.

Many project-specific languages, including in particular filtering languages, are defined using nonformal specifications written in natural languages. This leads to ambiguities and errors in the specification of those languages. This paper reports on an industrial experiment on using a tool-supported language specification framework (K) for the formal specification of the syntax and semantics of a filtering language having a complexity similar to those of real-life projects. This experimentation aims at estimating, in a specific industrial setting, the difficulty and benefits of formally specifying a packet filtering language using a tool-supported formal approach.

The use of domain-specific languages (DSLs) has become a successful technique in the development of complex systems. Consequently, nowadays we can find a large variety of DSLs for diverse purposes. However, not all these DSLs are completely different; many of them share certain commonalities coming from similar modeling patterns – such as state machines or petri nets – used for several purposes. In this scenario, the challenge for language designers is to take advantage of the commonalities existing among similar DSLs by reusing, as much as possible, formerly defined language constructs. The objective is to leverage previous engineering efforts to minimize implementation from scratch. To this end, recent research in software language engineering proposes the use of product line engineering, thus introducing the notion of language product lines. Nowadays, there are several approaches that result useful in the construction of language product lines. In this article, we report on an effort for organizing the literature on language product line engineering. More precisely, we propose a definition for the life-cycle of language product lines, and we use it to analyze the capabilities of current approaches. In addition, we provide a mapping between each approach and the technological space it supports.

The use of domain-specific languages (DSLs) has become a successful technique in the development of complex systems. Nevertheless, the construction of this type of languages is time-consuming and requires highly-specialized knowledge and skills. An emerging practice to facilitate this task is to enable reuse through the definition of lan-guage modules which can be later put together to build up new DSLs. Still, the identification and definition of language modules are complex and errorprone activities, thus hindering the reuse exploitation when developing DSLs. In this paper, we propose a computer-aided approach to (i) identify potential reuse in a set of legacy DSLs; and (ii) capitalize such potential reuse by extracting a set of reusable language modules with well defined interfaces that facilitate their assembly. We validate our approach by using realistic DSLs coming out from industrial case studies and obtained from public GitHub repositories.

The use of domain-specific languages (DSLs) has become a successful technique in the development of complex systems. Consequently, nowadays we can find a large variety of DSLs for diverse purposes. However, not all these DSLs are completely different; many of them share certain commonalities coming from similar modeling patterns – such as state machines or petri nets – used for several purposes. In this scenario, the challenge for language designers is to take advantage of the commonalities existing among similar DSLs by reusing, as much as possible, formerly defined language constructs. The objective is to leverage previous engineering efforts to minimize implementation from scratch. To this end, recent research in software language engineering proposes the use of product line engineering, thus introducing the notion of language product lines. Nowadays, there are several approaches that result useful in the construction of language product lines. In this article, we report on an effort for organizing the literature on language product line engineering. More precisely, we propose a definition for the life-cycle of language product lines, and we use it to analyze the capabilities of current approaches. In addition, we provide a mapping between each approach and the technological space it supports.