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Marr2022

Stefan Marr, Humphrey Burchell, and Fabio Niephaus. 2022. Execution vs. Parse-Based Language Servers: Tradeoffs and Opportunities for Language-Agnostic Tooling for Dynamic Languages. In Proceedings of the 18th ACM SIGPLAN International Symposium on Dynamic Languages (DLS 2022). Association for Computing Machinery, New York, NY, USA, 1–14.

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Abstract

With the wide adoption of the language server protocol, the desire to have IDE-style tooling even for niche and research languages has exploded. The Truffle language framework facilitates this desire by offering an almost zero-effort approach to language implementers to providing IDE features. However, this existing approach needs to execute the code being worked on to capture much of the information needed for an IDE, ideally with full unit-test coverage.

To capture information more reliably and avoid the need to execute the code being worked on, we propose a new parse-based design for language servers. Our solution provides a language-agnostic interface for structural information, with which we can support most common IDE features for dynamic languages.

Comparing the two approaches, we find that our new parse-based approach requires only a modest development effort for each language and has only minor tradeoffs for precision, for instance for code completion, compared to Truffle's execution-based approach.

Further, we show that less than 1,000 lines of code capture enough details to provide much of the typical IDE functionality, with an order of magnitude less code than ad hoc language servers. We tested our approach for the custom parsers of Newspeak and SOM, as well as SimpleLanguage's ANTLR grammar without any changes to it. Combining both parse and execution-based approaches has the potential to provide good and precise IDE tooling for a wide range of languages with only small development effort. By itself, our approach would be a good addition to the many libraries implementing the language server protocol to enable low-effort implementations of IDE features.