Principled syntactic code completion enables developers to change source code by inserting code templates, thus increasing developer efficiency and supporting language exploration. However, existing code completion systems are ad-hoc and neither complete nor sound. They are not complete and only provide few code templates for selected programming languages. They also are not sound and propose code templates that yield invalid programs when inserted. This paper presents a generic framework that automatically derives complete and sound syntactic code completion from the syntax definition of arbitrary languages. A key insight of our work is to provide an explicit syntactic representation for incomplete programs using placeholders. This enables us to address the following challenges for code completion separately: (i) completing incomplete programs by replacing placeholders with code templates, (ii) injecting placeholders into complete programs to make them incomplete, and (iii) introducing lexemes and placeholders into incorrect programs through error-recovery parsing to make them correct so we can apply one of the previous strategies. We formalize our framework and provide an implementation in Spoofax.

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.

Conducting analytics over data generated by Social Web portals such as Twitter is challenging, due to the volume, variety and velocity of the data. Commonly, adhoc pipelines are used that solve a particular use case. In this paper, we generalize across a range of typical Twitter-data use cases and determine a set of common characteristics. Based on this investigation, we present our Twitter Analytical Platform (TAP), a generic platform for conducting analytical tasks with Twitter data. The platform provides a domain-specific Twitter Analysis Language (TAL) as the interface to its functionality stack. TAL includes a set of analysis tools ranging from data collection and semantic enrichment, to machine learning. With these tools, it becomes possible to create and customize analytical workflows in TAL and build applications that make use of the analytics results. We showcase the applicability of our platform by building Twinder-a search engine for Twitter streams.

We describe a language-independent theory for name binding and resolution, suitable for programming languages with complex scoping rules including both lexical scoping and modules. We formulate name resolution as a two-stage problem. First a language-independent scope graph is constructed using language-specific rules from an abstract syntax tree. Then references in the scope graph are resolved to corresponding declarations using a language-independent resolution process. We introduce a resolution calculus as a concise, declarative, and language-independent specification of name resolution. We develop a resolution algorithm that is sound and complete with respect to the calculus. Based on the resolution calculus we develop language-independent definitions of α-equivalence and rename refactoring. We illustrate the approach using a small example language with modules. In addition, we show how our approach provides a model for a range of name binding patterns in existing languages.