Zipper-based embedding of modern attribute grammar extensions

This research abstract describes the research plan for a Ph.D project. We plan to define a powerful and elegant embedding of modern extensions to attribute grammars. Attribute grammars are a suitable formalism to express complex, multiple traversal algorithms. In recent years there has been a lot of work in attribute grammars, namely by defining new extensions to the formalism (forwarding and reference attribute grammars, etc), by proposing new attribute evaluation models (lazy and circular evaluators, etc) and by embedding attribute grammars (like first class attribute grammars). We will study how to design such extensions through a zipper-based embedding and we will study eficient evaluation models for this embedding. Finally, we will express several attribute grammars in our setting and we will analyse the performance of our implementation.(undefined

Memoized zipper-based attribute grammars and their higher order extension

Attribute grammars are a powerfull, well-known formalism to implement and reason about programs which, by design, are conveniently modular. In this work we focus on a state of the art zipper-based embedding of classic attribute grammars and higher-order attribute grammars. We improve their execution performance through controlling attribute (re)evaluation by means of memoization techniques. We present the results of our optimizations by comparing their impact in various implementations of different, well-studied, attribute grammars and their Higher-Order extensions. (C) 2018 Elsevier B.V. All rights reserved.- (undefined

Machine learning : techniques and foundations

The field of machine learning studies computational methods for acquiring new knowledge, new skills, and new ways to organize existing knowledge. In this paper we present some of the basic techniques and principles that underlie AI research on learning, including methods for learning from examples, learning in problem solving, learning by analogy, grammar acquisition, and machine discovery. In each case, we illustrate the techniques with paradigmatic examples

Incremental Evaluation of Reference Attribute Grammars using Dynamic Dependency Tracking

Reference attribute grammars (RAGs) have proven practical for gen- erating production-quality compilers from declarative descriptions, as demonstrated by the JastAdd system. Recent results indicate their applicability also to generating semantic services in interactive editors. For use in editors, it is necessary to update the attribution after edit operations. Earlier algorithms based on statically scheduled incremental attribute evaluation are, however, not applicable to RAGs, as they do not account for the dynamic dependencies that reference attributes give rise to. In this report, we introduce a notion of consistency for RAG attributions, along with an algorithm for maintaining consistency after edit operations, based on dynamic dependency tracking. That is, we introduce a means to do incremental evaluation of RAGs using dynamic dependency tracking

Contributions to the Construction of Extensible Semantic Editors

This dissertation addresses the need for easier construction and extension of language tools. Specifically, the construction and extension of so-called semantic editors is considered, that is, editors providing semantic services for code comprehension and manipulation. Editors like these are typically found in state-of-the-art development environments, where they have been developed by hand. The list of programming languages available today is extensive and, with the lively creation of new programming languages and the evolution of old languages, it keeps growing. Many of these languages would benefit from proper tool support. Unfortunately, the development of a semantic editor can be a time-consuming and error-prone endeavor, and too large an effort for most language communities. Given the complex nature of programming, and the huge benefits of good tool support, this lack of tools is problematic. In this dissertation, an attempt is made at narrowing the gap between generative solutions and how state-of-the-art editors are constructed today. A generative alternative for construction of textual semantic editors is explored with focus on how to specify extensible semantic editor services. Specifically, this dissertation shows how semantic services can be specified using a semantic formalism called refer- ence attribute grammars (RAGs), and how these services can be made responsive enough for editing, and be provided also when the text in an editor is erroneous. Results presented in this dissertation have been found useful, both in industry and in academia, suggesting that the explored approach may help to reduce the effort of editor construction

Extending Attribute Grammars to Support Programming-in-the-Large

Attribute grammars add specification of static semantic properties to context-free grammars, which in turn describe the syntactic structure of program units. However, context-free grammars cannot express programming-in-the-large features common in modern programming languages, including unordered collections of units, included units and sharing of included units. We present extensions to context-free grammars, and corresponding extensions to attribute grammars, suitable for defining such features. We explain how batch and incremental attribute evaluation algorithms can be adapted to support these extensions, resulting in a uniform approach to intra-unit and inter-unit static semantic analysis and translation of multi-unit programs