Ambiguity Detection: Scaling to Scannerless

Static ambiguity detection would be an important aspect of language workbenches for textual software languages. However, the challenge is that automatic ambiguity detection in context-free grammars is undecidable in general. Sophisticated approximations and optimizations do exist, but these do not scale to grammars for so-called ``scannerless parsers'', as of yet. We extend previous work on ambiguity detection for context-free grammars to cover disambiguation techniques that are typical for scannerless parsing, such as longest match and reserved keywords. This paper contributes a new algorithm for ambiguity detection in character-level grammars, a prototype implementation of this algorithm and validation on several real grammars. The total run-time of ambiguity detection for character-level grammars for languages such as C and Java is significantly reduced, without loss of precision. The result is that efficient ambiguity detection in realistic grammars is possible and may therefore become a tool in language workbenches

Ambiguity Detection: Scaling to Scannerless

Static ambiguity detection would be an important aspect of language workbenches for textual software languages. However, the challenge is that automatic ambiguity detection in context-free grammars is undecidable in general. Sophisticated approximations and optimizations do exist, but these do not scale to grammars for so-called ``scannerless parsers'', as of yet. We extend previous work on ambiguity detection for context-free grammars to cover disambiguation techniques that are typical for scannerless parsing, such as longest match and reserved keywords. This paper contributes a new algorithm for ambiguity detection in character-level grammars, a prototype implementation of this algorithm and validation on several real grammars. The total run-time of ambiguity detection for character-level grammars for languages such as C and Java is significantly reduced, without loss of precision. The result is that efficient ambiguity detection in realistic grammars is possible and may therefore become a tool in language workbenches

Operator precedence for data-dependent grammars

Constructing parsers based on declarative specification of operator precedence is a very old research topic, and there are various existing approaches. However, these approaches are either tied to a particular parsing technique, or cannot deal with all corner cases found in programming languages. In this paper we present an implementation of declarative specification of operator precedence for general parsing that (1) is independent of the underlying parsing algorithm, (2) does not require any grammar transformation that increases the size of the grammar, (3) preserves the shape of parse trees of the original, natural grammar, and (4) can deal with intricate cases of operator precedence found in functional programming languages such as OCaml. Our new approach to operator precedence is formulated using data-dependent grammars, which extend context-free grammars with arbitrary computation, variable binding and constraints. We implemented our approach using Iguana, a data-dependent parsing framework, and evaluated it by parsing Java and OCaml source files. The results show that our approach is practical for parsing programming languages with complicated operator precedence rules

flap: A Deterministic Parser with Fused Lexing

Lexers and parsers are typically defined separately and connected by a token stream. This separate definition is important for modularity and reduces the potential for parsing ambiguity. However, materializing tokens as data structures and case-switching on tokens comes with a cost. We show how to fuse separately-defined lexers and parsers, drastically improving performance without compromising modularity or increasing ambiguity. We propose a deterministic variant of Greibach Normal Form that ensures deterministic parsing with a single token of lookahead and makes fusion strikingly simple, and prove that normalizing context free expressions into the deterministic normal form is semantics-preserving. Our staged parser combinator library, flap, provides a standard interface, but generates specialized token-free code that runs two to six times faster than ocamlyacc on a range of benchmarks.Comment: PLDI 2023 with appendi

Context-Aware Scanning for Parsing Extensible Languages

Associated research group: Minnesota Extensible Language ToolsThis paper introduces new parsing and context-aware scanning algorithms in which the scanner uses contextual information to disambiguate lexical syntax. The parser utilizes a slightly modified LR-style algorithm that passes to the scanner the set of valid symbols which the scanner may return at that point in parsing. This set is the terminal symbols that are valid for the current state, i.e., those whose entry in the parse table are shift, reduce, or accept, but not error. The scanner then only returns tokens in this set. Also, an analysis is given that can statically verify that the scanner will never return more than one token for a single input. Context-aware scanning is especially useful when parsing and scanning extensible languages in which domain specific languages can be embedded. We illustrate this approach with a declarative specification of a Java subset and extensions that embed SQL queries and Boolean expression tables into Java