Language workbenches are touted as a promising technology to engineer languages for use in a wide range of domains, from programming to science to business. However, not many real-world case studies exist that evaluate the suitability of language workbench technology for this task. This paper contains such a case study. In particular, we evaluate the development of mbeddr, a collection of integrated languages and language extensions built with the Jetbrains MPS language workbench. mbeddr consists of 81 languages, with their IDE support, 34 of them C extensions. The mbeddr languages use a wide variety of notations---textual, tabular, symbolic and graphical---and the C extensions are modular; new extensions can be added without changing the existing implementation of C. mbeddr's development has spanned 10 person-years so far, and the tool is used in practice and continues to be developed. This makes mbeddr a meaningful case study of non-trivial size and complexity. The evaluation is centered around five research questions: language modularity, notational freedom and projectional editing, mechanisms for managing complexity, performance and scalability issues and the consequences for the development process. We draw generally positive conclusions; language engineering with MPS is ready for real-world use. However, we also identify a number of areas for improvement in the state of the art in language engineering in general, and in MPS in particular.@en

Program analyses support software developers, for example, through error detection, code-quality assurance, and by enabling compiler optimizations and refactorings. To provide real-time feedback to developers within IDEs, an analysis must run efficiently even if the analyzed code base is large. To achieve this goal, we present a domain-specific language called IncA for the definition of efficient incremental program analyses that update their result as the program changes. IncA compiles analyses into graph patterns and relies on existing incremental matching algorithms. To scale IncA analyses to large programs, we describe optimizations that reduce caching and prune change propagation. Using IncA, we have developed incremental control flow and points-to analysis for C, well-formedness checks for DSLs, and 10 FindBugs checks for Java. Our evaluation demonstrates significant speedups for all analyses compared to their non-incremental counterparts.@en

Language workbenches are widely used to implement domain-specific languages (DSLs) and their accompanying integrated development environments (IDEs). They help to define the abstract syntax, concrete syntax(es), type system, and transformations for the languages. However, there are other language aspects, specifically program analyses and optimizations, that are also crucial to a language implementation, but state-of-the-art language workbenches has only limited support for them. The high implementation effort for these language aspects is justifiable for a general-purpose language (GPL), but is not justifiable for DSLs because of their different development economies. To this end, I conduct research on dedicated support for analyses and optimizations for DSLs in language workbenches. My main goal is to develop declarative meta-languages that help to define static program analyses and that capture and automate patterns and techniques of optimizations. The research directions are directly driven by industrial need, and upon successful completion, the results would be applied in projects centered around DSLs for high-performance computing (HPC), insurance, and concurrent embedded systems. @en

The definition of a projectional editor does not just specify the notation of a language, but also how users interact with the notation. Because of that it is easy to end up with different interaction styles within one and between multiple languages. The resulting inconsistencies have proven to be a major usability problem. To address this problem, we introduce grammar cells, an approach for declaratively specifying textual notations and their interactions for projectional editors. In the paper we motivate the problem, give a formal definition of grammar cells, and define their mapping to low-level editor behaviors. Our evaluation based on project experience shows that grammar cells improve editing experience by providing a consistent and intuitive ``text editor-like'' user experience for textual notations. At the same time they do not limit language composability and the use of non-textual notations, the primary benefits of projectional editors. We have implemented grammar cells for Jetbrains MPS, but they can also be used with other projectional editors.@en

The definition of a projectional editor does not just specify the notation of a language, but also how users interact with the notation. Because of that it is easy to end up with different interaction styles within one and between multiple languages. The resulting inconsistencies have proven to be a major usability problem. To address this problem, we introduce grammar cells, an approach for declaratively specifying textual notations and their interactions for projectional editors. In the paper we motivate the problem, give a formal definition of grammar cells, and define their mapping to low-level editor behaviors. Our evaluation based on project experience shows that grammar cells improve editing experience by providing a consistent and intuitive ``text editor-like'' user experience for textual notations. At the same time they do not limit language composability and the use of non-textual notations, the primary benefits of projectional editors. We have implemented grammar cells for Jetbrains MPS, but they can also be used with other projectional editors.@en

Data-flow analyses are used as part of many software engineering tasks: they are the foundations of program under- standing, refactorings and optimized code generation. Similar to general-purpose languages (GPLs), state-of-the-art domain-specific languages (DSLs) also require sophisticated data-flow analyses. However, as a consequence of the different economies of DSL development and their typically relatively fast evolution, the effort for developing and evolving such analyses must be lowered compared to GPLs. This tension can be resolved with dedicated support for data-flow analyses in language workbenches. In this tool paper we present MPS-DF, which is the component in the MPS language workbench that supports the definition of data-flow analyses for DSLs. Language developers can define data-flow graph builders declaratively as part of a language definition and compute analysis results efficiently based on these data-flow graphs. MPS-DF is extensible such that it does not compromise the support for language composition in MPS. Additionally, clients of MPS-DF analyses can run the analyses with variable precision thus trading off precision for performance. This allows clients to tailor an analysis to a particular use case. @en

Object-oriented programming languages feature static and dynamic overloading: Multiple methods share the same name but provide different implementations. Dynamic overloading (also know as dynamic dispatch) is resolved at run time based on the type of the receiver object. In this paper, we focus on static overloading, which is resolved at compile time based on the types of the method arguments. The challenge this paper addresses is to incrementalize static overload resolution in IDEs. IDEs resolve overloaded methods for the developer to help them discern which implementation a method call refers to. However, as the code changes, the IDE has to reconsider previously resolved method calls when they are affected by the code change. This paper clarifies when a method call is affected by a code change and how to re-resolve method calls with minimal computational effort. To this end, we explore and compare two approaches to incremental type checking: co-contextual type checking and IncA.@en

Program analyses detect errors in code, but when code changes frequently as in an IDE, repeated re-analysis from-scratch is unnecessary: It leads to poor performance unless we give up on precision and recall. Incremental program analysis promises to deliver fast feedback without giving up on precision or recall by deriving a new analysis result from the previous one. However, Datalog and other existing frameworks for incremental program analysis are limited in expressive power: They only support the powerset lattice as representation of analysis results, whereas many practically relevant analyses require custom lattices and aggregation over lattice values. To this end, we present a novel algorithm called DRedL that supports incremental maintenance of recursive lattice-value aggregation in Datalog. The key insight of DRedL is to dynamically recognize increasing replacements of old lattice values by new ones, which allows us to avoid the expensive deletion of the old value. We integrate DRedL into the analysis framework IncA and use IncA to realize incremental implementations of strong-update points-to analysis and string analysis for Java. As our performance evaluation demonstrates, both analyses react to code changes within milliseconds.@en