Anastasia Izmaylova

One parser to rule them all

Despite the long history of research in parsing, constructing parsers for real programming languages remains a difficult and painful task. In the last decades, different parser generators emerged to allow the construction of parsers from a BNF-like specification. However, still today, many parsers are handwritten, or are only partly generated, and include various hacks to deal with different peculiarities in programming languages. The main problem is that current declarative syntax definition techniques are based on pure context-free grammars, while many constructs found in programming languages require context information. In this paper we propose a parsing framework that embraces context information in its core. Our framework is based on data-dependent grammars, which extend context-free grammars with arbitrary computation, variable binding and constraints. We present an implementation of our framework on top of the Generalized LL (GLL) parsing algorithm, and show how common idioms in syntax of programming languages such as (1) lexical disambiguation filters, (2) operator precedence, (3) indentation-sensitive rules, and (4) conditional preprocessor directives can be mapped to data-dependent grammars. We demonstrate the initial experience with our framework, by parsing more than 20000 Java, C#, Haskell, and OCaml source files.

Faster, Practical GLL Parsing

Generalized LL (GLL) parsing is an extension of recursivedescent (RD) parsing that supports all context-free grammars in cubic time and space. GLL parsers have the direct relationship with the grammar that RD parsers have, and therefore, compared to GLR, are easier to understand, debug, and extend. This makes GLL parsing attractive for parsing programming languages.

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.

Practical, general parser combinators

Parser combinators are a popular approach to parsing where context-free grammars are represented as executable code. However, conventional parser combinators do not support left recursion, and can have worst-case exponential runtime. These limitations hinder the expressivity and performance predictability of parser combinators when constructing parsers for programming languages. In this paper we present general parser combinators that support all context-free grammars and construct a parse forest in cubic time and space in the worst case, while behaving nearly linearly on grammars of real programming languages. Our general parser combinators are based on earlier work on memoized Continuation-Passing Style (CPS) recognizers. First, we extend this work to achieve recognition in cubic time. Second, we extend the resulting cubic CPS recognizers to parsers that construct a binarized Shared Packed Parse Forest (SPPF). Our general parser combinators bring the best of both worlds: the flexibility and extensibility of conventional parser combinators and the expressivity and performance guarantees of general parsing algorithms. We used the approach presented in this paper as the basis for Meerkat, a general parser combinator library for Scala.

Iguana: a practical data-dependent parsing framework

Data-dependent grammars extend context-free grammars with arbitrary computation, variable binding, and constraints. These features provide the user with the freedom and power to express syntactic constructs outside the realm of context-free grammars, e.g., indentation rules in Haskell and type definitions in C. Data-dependent grammars have been recently presented by Jim et al. as a grammar formalism that enables construction of parsers from a rich format specification. Although some features of data-dependent grammars are available in current parsing tools, e.g., semantic predicates in ANTLR, data-dependent grammars have not yet fully found their way into practice. In this paper we present Iguana, a data-dependent parsing framework, implemented on top of the GLL parsing algorithm. In addition to basic features of data-dependent grammars, Iguana also provides high-level syntactic constructs, e.g., for operator precedence and indentation rules, which are implemented as desugaring to data-dependent grammars. These high-level constructs enable a concise and declarative way to define the syntax of programming languages. Moreover, Iguanas extensible data-dependent grammar API allows the user to easily add new high-level constructs or modify existing ones. We have used Iguana to parse various real programming languages, such as OCaml, Haskell, Java, and C#. In this paper we describe the architecture and features of Iguana, and report on its current implementation status.