Parsing is the process that enables a computer system to make sense of raw data. Parsing is common to almost all computer systems: It is involved every time sequential data is read and elaborated into structured data. The theory of parsing usually focuses on the binary recognition aspect of parsing and eschews this essential data-elaboration aspect. In this thesis, I present a declarative framework for value-aware parsing that explicitly integrates data elaboration.
Within the framework of the thesis, I present parsing algorithms that are based on the concept of Brzozowski's derivatives. Derivative-based parsing algorithms present several advantages: they are elegant, amenable to formal reasoning, and easy to implement. Unfortunately, the performance of these algorithms in practice is often not competitive with other approaches. In this thesis, I show a general technique inspired by Huet's Zipper to greatly enhance the performance of derivative-based algorithms, and I do so without compromising their elegance, amenability to formal reasoning, or ease of implementation.
First, I present a technique for building efficient tokenisers that is based on Brzozowski's derivatives and Huet's zipper and that does not require the usual burdensome explicit conversion to automata. I prove the technique is correct in Coq and present SILEX, a Scala lexing library based on the technique. I demonstrate that the approach is competitive with state-of-the-art solutions.
Then, I present a characterisation of LL(1) languages based on the concept of should-not-follow sets. I present an algorithm for parsing LL(1) languages with derivatives and zippers. I show a formal proof of the algorithm's correctness and prove its worst-case linear-time complexity. I show how the LL(1) parsing with derivatives and zippers algorithm corresponds to the traditional LL(1) parsing algorithm.
I then present SCALL1ON, a Scala parsing combinators library for LL(1) languages that incorporates the LL(1) parsing with derivatives and zippers algorithm. I present an expressive and familiar combinator-based interface for describing LL(1) languages. I present techniques that help precisely locate LL(1) conflicts in user code. I discuss several advantages of the parsing with derivatives approach within the context of a parsing library. I also present SCALL1ON's enumeration and pretty-printing features and discuss their implementation. Through a series of benchmarks, I demonstrate the good performance and practicality of the approach. Finally, I present how to adapt the LL(1) parsing with derivatives and zippers algorithm to support arbitrary context-free languages. I show how the adapted algorithm corresponds to general parsing algorithms, such as Earley's parsing algorithm.