Providing rapid feedback in generated modular language environments

Abstract

Integrated development environments (IDEs) increase programmer productivity, providing rapid, interactive feedback based on the syntax and semantics of a language. A heavy burden lies on developers of new languages to provide adequate IDE support. Code generation techniques provide a viable, efficient approach to semi-automatically produce IDE plugins. Key components for the realization of plugins are the language's grammar and parser. For embedded languages and language extensions, constituent IDE plugin modules and their grammars can be combined. Unlike conventional parsing algorithms, scannerless generalized-LR parsing supports the full set of context-free grammars, which is closed under composition, and hence can parse language embeddings and extensions composed from separate grammar modules. To apply this algorithm in an interactive environment, this paper introduces a novel error recovery mechanism, which allows it to be used with files with syntax errors -common in interactive editing. Error recovery is vital for providing rapid feedback in case of syntax errors, as most IDE services depend on the parser -from syntax highlighting to semantic analysis and cross-referencing. We base our approach on the principles of island grammars, and derive permissive grammars with error recovery productions from normal SDF grammars. To cope with the added complexity of these grammars, we adapt the parser to support backtracking. We evaluate the recovery quality and performance of our approach using a set of composed languages, based on Java and Stratego.

Natural and Flexible Error Recovery for Generated Parsers

“…We implemented the region selection method in SGLR, in order to use it in collaboration with recovery rules [12]. The selection method does not depend on specific features of generalized parsing and can be implemented in other LR parsers as well.…”

“…In this section we outline how the error recovery productions of [12] can be used to perform fine-grained error recovery inside erroneous regions. Error recovery productions allow for a high-level, grammar-oriented way of customizing a recovery strategy [2,10].…”

“…Some parser generators allow custom recovery rules to improve error recovery quality [2,5,10,12]. Custom recovery rules allow a language engineer to inspect and improve an error recovery strategy.…”

“…In previous work we introduced an approach to error recovery that derives properties from grammars to produce explicit, customizable recovery rules [12]. Using scannerless generalized-LR (SGLR) parsing, the approach supports languages with a complex lexical syntax, such as AspectJ [3], and language embeddings and extensions, such as the Stratego program transformation language with embedded Java fragments [30].…”

“…An open problem we identified with our approach is that some search space-based suggestions are too "creative" and not natural (i.e., as a programmer would suggest them) [12]; in some cases it is simply better to ignore a small part of the input file, rather than to try and fix it using a combination of insertions and discarded substrings. Another open problem is that for tight clusters of errors, it is not always feasible to provide good suggestions in an acceptable time span.…”