On Solving Solved Problems

Some problems are considered solved by the research community. But are they really and does that mean we should stop investigating them? In this essay, I argue that "solved" problems often only appear solved on the surface, while fundamental open research problems lurk below the surface. It requires dedicated researchers to discover those open problems by applying the existing solutions and putting them to the test

Spoofax at Oracle: Domain-Specific Language Engineering for Large-Scale Graph Analytics

For the last decade, teams at Oracle relied on the Spoofax language workbench to develop a family of domain-specific languages for graph analytics in research projects and in product development. In this paper, we analyze the requirements for integrating language processors into large-scale graph analytics toolkits and for the development of these language processors as part of a larger product development process. We discuss how Spoofax helps to meet these requirements and point out the need for future improvements

Reusable textual styles for domain-specific modeling languages

Domain-specific languages enable concise and precise formalization of domain concepts and promote direct employment by domain experts. Therefore, syntactic constructs are introduced to empower users to associate concepts and relationships with visual textual symbols. Model-based language engineering facilitates the description of concepts and relationships in an abstract manner. However, concrete representations are commonly attached to abstract domain representations, such as annotations in metamodels, or directly encoded into language grammar and thus introduce redundancy between metamodel elements and grammar elements. In this work we propose an approach that enables autonomous development and maintenance of domain concepts and textual language notations in a distinctive and metamodel-agnostic manner by employing style models containing grammar rule templates and injection-based property selection. We provide an implementation and showcase the proposed notationspecification language in a comparison with state of the art practices during the creation of notations for an executable domain-specific modeling language based on the Eclipse Modeling Framework and Xtext

Languages of games and play: A systematic mapping study

Digital games are a powerful means for creating enticing, beautiful, educational, and often highly addictive interactive experiences that impact the lives of billions of players worldwide. We explore what informs the design and construction of good games to learn how to speed-up game development. In particular, we study to what extent languages, notations, patterns, and tools, can offer experts theoretical foundations, systematic techniques, and practical solutions they need to raise their productivity and improve the quality of games and play. Despite the growing number of publications on this topic there is currently no overview describing the state-of-the-art that relates research areas, goals, and applications. As a result, efforts and successes are often one-off, lessons learned go overlooked, language reuse remains minimal, and opportunities for collaboration and synergy are lost. We present a systematic map that identifies relevant publications and gives an overview of research areas and publication venues. In addition, we categorize research perspectives along common objectives, techniques, and approaches, illustrated by summaries of selected languages. Finally, we distill challenges and opportunities for future research and development

Towards Zero-Overhead Disambiguation of Deep Priority Conflicts

**Context** Context-free grammars are widely used for language prototyping and implementation. They allow formalizing the syntax of domain-specific or general-purpose programming languages concisely and declaratively. However, the natural and concise way of writing a context-free grammar is often ambiguous. Therefore, grammar formalisms support extensions in the form of *declarative disambiguation rules* to specify operator precedence and associativity, solving ambiguities that are caused by the subset of the grammar that corresponds to expressions. **Inquiry** Implementing support for declarative disambiguation within a parser typically comes with one or more of the following limitations in practice: a lack of parsing performance, or a lack of modularity (i.e., disallowing the composition of grammar fragments of potentially different languages). The latter subject is generally addressed by scannerless generalized parsers. We aim to equip scannerless generalized parsers with novel disambiguation methods that are inherently performant, without compromising the concerns of modularity and language composition. **Approach** In this paper, we present a novel low-overhead implementation technique for disambiguating deep associativity and priority conflicts in scannerless generalized parsers with lightweight data-dependency. **Knowledge** Ambiguities with respect to operator precedence and associativity arise from combining the various operators of a language. While *shallow conflicts* can be resolved efficiently by one-level tree patterns, *deep conflicts* require more elaborate techniques, because they can occur arbitrarily nested in a tree. Current state-of-the-art approaches to solving deep priority conflicts come with a severe performance overhead. **Grounding** We evaluated our new approach against state-of-the-art declarative disambiguation mechanisms. By parsing a corpus of popular open-source repositories written in Java and OCaml, we found that our approach yields speedups of up to 1.73x over a grammar rewriting technique when parsing programs with deep priority conflicts--with a modest overhead of 1-2 % when parsing programs without deep conflicts. **Importance** A recent empirical study shows that deep priority conflicts are indeed wide-spread in real-world programs. The study shows that in a corpus of popular OCaml projects on Github, up to 17 % of the source files contain deep priority conflicts. However, there is no solution in the literature that addresses efficient disambiguation of deep priority conflicts, with support for modular and composable syntax definitions

The program is the model: Enabling [email protected]

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-36089-3_7Revised Selected Papers of 5th International Conference, SLE 2012, Dresden, Germany, September 26-28, 2012The increasing application of Model-Driven Engineering in a wide range of domains, in addition to pure code generation, raises the need to manipulate models at runtime, as part of regular programs. Moreover, certain kinds of programming tasks can be seen as model transformation tasks, and thus we could take advantage of model transformation technology in order to facilitate them. In this paper we report on our works to bridge the gap between regular programming and model transformation by enabling the manipulation of Java APIs as models. Our approach is based on the specification of a mapping between a Java API (e.g., Swing) and a meta-model describing it. A model transformation definition is written against the API meta-model and we have built a compiler that generates the corresponding Java bytecode according to the mapping. We present several application scenarios and discuss the mapping between object-oriented meta-modelling and the Java object system. Our proposal has been validated by a prototype implementation which is also contributed.Work funded by the Spanish Ministry of Economy and Competitivity (TIN2011-24139), and the R&D programme of Madrid Region (S2009/TIC-1650)

HyperAST: Enabling Efficient Analysis of Software Histories at Scale

International audienceSyntax Trees (ASTs) are widely used beyond compilers in many tools that measure and improve code quality, such as code analysis, bug detection, mining code metrics, refactoring. With the advent of fast software evolution and multistage releases, the temporal analysis of an AST history is becoming useful to understand and maintain code. However, jointly analyzing thousands versions of ASTs independently faces scalability issues, mostly combinatorial, both in terms of memory and CPU usage. In this paper, we propose a novel type of AST, called HyperAST , that enables efficient temporal code analysis on a given software history by: 1/ leveraging code redundancy through space (between code elements) and time (between versions); 2/ reusing intermediate computation results. We show how the HyperAST can be built incrementally on a set of commits to capture all multiple ASTs at once in an optimized way. We evaluated the HyperAST on a curated list of large software projects. Compared to Spoon, a state-of-the-art technique, we observed that the HyperAST outperforms it with an order-of-magnitude difference from ×6 up to ×8076 in CPU construction time and from ×12 up to ×1159 in memory footprint. While the HyperAST requires up to 2 h 22 min and 7.2 GB for the biggest project, Spoon requires up to 93 h and 31 min and 2.2 TB. The gains in construction time varied from 83.4 % to 99.99 % and the gains in memory footprint varied from 91.8 % to 99.9 %. We further compared the task of finding references of declarations with the HyperAST and Spoon. We observed on average 90 % precision and 97 % recall without a significant difference in search time