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Language workbenches are environments for simplifying the creation and use of computer languages. The annual Language Workbench Challenge (LWC) was launched in 2011 to allow the many academic and industrial researchers in this area an opportunity to quantitatively and qualitatively compare their approaches. We first describe all four LWCs to date, before focussing on the approaches used, and results generated, during the third LWC. We give various empirical data for ten approaches from the third LWC. We present a generic feature model within which the approaches can be understood and contrasted. Finally, based on our experiences of the existing LWCs, we propose a number of benchmark problems for future LWCs.

The goal of the DSLDI workshop is to bring together researchers and practitioners interested in sharing ideas on how DSLs should be designed, implemented, supported by tools, and applied in realistic application contexts. We are both interested in discovering how already known domains such as graph processing or machine learning can be best supported by DSLs, but also in exploring new domains that could be targeted by DSLs. More generally, we are interested in building a community that can drive forward the development of modern DSLs. These informal post-proceedings contain the submitted talk abstracts to the 3rd DSLDI workshop (DSLDI'15), and a summary of the panel discussion on Language Composition.

We present an algorithm called name-fix that automatically eliminates variable capture from a generated program by systematically renaming variables. name-fix is guided by a graph representation of the binding structure of a program, and requires name-resolution algorithms for the source language and the target language of a transformation. name-fix is generic and works for arbitrary transformations in any transformation system that supports origin tracking for names.

Language workbenches are environments for simplifying the creation and use of computer languages. The annual Language Workbench Challenge (LWC) was launched in 2011 to allow the many academic and industrial researchers in this area an opportunity to quantitatively and qualitatively compare their approaches. We first describe all four LWCs to date, before focussing on the approaches used, and results generated, during the third LWC. We give various empirical data for ten approaches from the third LWC. We present a generic feature model within which the approaches can be understood and contrasted. Finally, based on our experiences of the existing LWCs, we propose a number of benchmark problems for future LWCs.

The goal of the DSLDI workshop is to bring together researchers and practitioners interested in sharing ideas on how DSLs should be designed, implemented, supported by tools, and applied in realistic application contexts. We are both interested in discovering how already known domains such as graph processing or machine learning can be best supported by DSLs, but also in exploring new domains that could be targeted by DSLs. More generally, we are interested in building a community that can drive forward the development of modern DSLs. These informal post-proceedings contain the submitted talk abstracts to the 3rd DSLDI workshop (DSLDI'15), and a summary of the panel discussion on Language Composition.

We present an algorithm called name-fix that automatically eliminates variable capture from a generated program by systematically renaming variables. name-fix is guided by a graph representation of the binding structure of a program, and requires name-resolution algorithms for the source language and the target language of a transformation. name-fix is generic and works for arbitrary transformations in any transformation system that supports origin tracking for names.

Program transformations play an important role in domain-specific languages and model-driven development. Tracing the execution of such transformations has well-known benefits for debugging, visualization and error reporting. In this paper we introduce string origins as a lightweight, generic and portable technique to establish a tracing relation between the textual fragments in the input and output of a program transformation. We discuss the semantics and the implementation of string origins using the Rascal meta programming language as an example. Furthermore, we illustrate the utility of string origins by presenting data structures and operations for tracing generated code, implementing protected regions, performing name resolution, and fixing inadvertent name capture in generated code.

Program transformations in terms of abstract syntax trees compromise referential integrity by introducing variable capture. Variable capture occurs when in the generated program a variable declaration accidentally shadows the intended target of a variable reference. Existing transformation systems either do not guarantee the avoidance of variable capture or impair the implementation of transformations. We present an algorithm called name-fix that automatically eliminates variable capture from a generated program by systematically renaming variables. name-fix is guided by a graph representation of the binding structure of a program, and requires name-resolution algorithms for the source language and the target language of a transformation. name-fix is generic and works for arbitrary transformations in any transformation system that supports origin tracking for names. We verify the correctness of name-fix and identify an interesting class of transformations for which name-fix provides hygiene. We demonstrate the applicability of name-fix for implementing capture-avoiding substitution, inlining, lambda lifting, and compilers for two domain-specific languages.

Program transformations play an important role in domain-specific languages and model-driven development. Tracing the execution of such transformations has well-known benefits for debugging, visualization and error reporting. In this paper we introduce string origins as a lightweight, generic and portable technique to establish a tracing relation between the textual fragments in the input and output of a program transformation. We discuss the semantics and the implementation of string origins using the Rascal meta programming language as an example. Furthermore, we illustrate the utility of string origins by presenting data structures and operations for tracing generated code, implementing protected regions, performing name resolution, and fixing inadvertent name capture in generated code.

Program transformations in terms of abstract syntax trees compromise referential integrity by introducing variable capture. Variable capture occurs when in the generated program a variable declaration accidentally shadows the intended target of a variable reference. Existing transformation systems either do not guarantee the avoidance of variable capture or impair the implementation of transformations. We present an algorithm called name-fix that automatically eliminates variable capture from a generated program by systematically renaming variables. name-fix is guided by a graph representation of the binding structure of a program, and requires name-resolution algorithms for the source language and the target language of a transformation. name-fix is generic and works for arbitrary transformations in any transformation system that supports origin tracking for names. We verify the correctness of name-fix and identify an interesting class of transformations for which name-fix provides hygiene. We demonstrate the applicability of name-fix for implementing capture-avoiding substitution, inlining, lambda lifting, and compilers for two domain-specific languages.

Language workbenches are tools that provide high-level mechanisms for the implementation of (domain-specific) languages. Language workbenches are an active area of research that also receives many contributions from industry. To compare and discuss existing language workbenches, the annual Language Workbench Challenge was launched in 2011. Each year, participants are challenged to realize a given domain-specific language with their workbenches as a basis for discussion and comparison. In this paper, we describe the state of the art of language workbenches as observed in the previous editions of the Language Workbench Challenge. In particular, we capture the design space of language workbenches in a feature model and show where in this design space the participants of the 2013 Language Workbench Challenge reside. We compare these workbenches based on a DSL for questionnaires that was realized in all workbenches.

Language workbenches are tools that provide high-level mechanisms for the implementation of (domain-specific) languages. Language workbenches are an active area of research that also receives many contributions from industry. To compare and discuss existing language workbenches, the annual Language Workbench Challenge was launched in 2011. Each year, participants are challenged to realize a given domain-specific language with their workbenches as a basis for discussion and comparison. In this paper, we describe the state of the art of language workbenches as observed in the previous editions of the Language Workbench Challenge. In particular, we capture the design space of language workbenches in a feature model and show where in this design space the participants of the 2013 Language Workbench Challenge reside. We compare these workbenches based on a DSL for questionnaires that was realized in all workbenches.