Derivation of a Typed Functional LR Parser
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Buffered Shift-Reduce Parsing*
BUFFERED SHIFT-REDUCE PARSING* Bing Swen (Sun Bin) Dept. of Computer Science, Peking University, Beijing 100871, China [email protected] Abstract A parsing method called buffered shift-reduce parsing is presented, which adds an intermediate buffer (queue) to the usual LR parser. The buffer’s usage is analogous to that of the wait-and-see parsing, but it has unlimited buffer length, and may serve as a separate reduction (pruning) stack. The general structure of its parser and some features of its grammars and parsing tables are discussed. 1. Introduction It is well known that the LR parsing is hitherto the most general shift-reduce method of bottom-up parsing [1], and is among the most widely used. For example, almost all compilers of mainstream programming languages employ the LR-like parsing (via an LALR(1) compiler generator such as YACC or GNU Bison [2]), and many NLP systems use a generalized version of LR parsing (GLR, also called the Tomita algorithm [3]). In some earlier research work on LR(k) extensions for NLP [4] and generalization of compiler generator [5], an idea naturally occurred that one can make a modification to the LR parsing so that after each reduction, the resulting symbol may not be necessarily pushed onto the analysis stack, but instead put back into the input, waiting for decisions in the following steps. This strategy postpones the time of some reduction actions in traditional LR parser, partly deviating from leftmost pruning (Leftmost Reduction). This may cause performance loss for some grammars, with the gained opportunities of earlier error detecting and avoidance of false reductions, as well as later handling of ambiguities, which is significant for the processing of highly ambiguous input strings like natural language sentences. -
Efficient Computation of LALR(1) Look-Ahead Sets
Efficient Computation of LALR(1) Look-Ahead Sets FRANK DeREMER and THOMAS PENNELLO University of California, Santa Cruz, and MetaWare TM Incorporated Two relations that capture the essential structure of the problem of computing LALR(1) look-ahead sets are defined, and an efficient algorithm is presented to compute the sets in time linear in the size of the relations. In particular, for a PASCAL grammar, the algorithm performs fewer than 15 percent of the set unions performed by the popular compiler-compiler YACC. When a grammar is not LALR(1), the relations, represented explicitly, provide for printing user- oriented error messages that specifically indicate how the look-ahead problem arose. In addition, certain loops in the digraphs induced by these relations indicate that the grammar is not LR(k) for any k. Finally, an oft-discovered and used but incorrect look-ahead set algorithm is similarly based on two other relations defined for the fwst time here. The formal presentation of this algorithm should help prevent its rediscovery. Categories and Subject Descriptors: D.3.1 [Programming Languages]: Formal Definitions and Theory--syntax; D.3.4 [Programming Languages]: Processors--translator writing systems and compiler generators; F.4.2 [Mathematical Logic and Formal Languages]: Grammars and Other Rewriting Systems--parsing General Terms: Algorithms, Languages, Theory Additional Key Words and Phrases: Context-free grammar, Backus-Naur form, strongly connected component, LALR(1), LR(k), grammar debugging, 1. INTRODUCTION Since the invention of LALR(1) grammars by DeRemer [9], LALR grammar analysis and parsing techniques have been popular as a component of translator writing systems and compiler-compilers. -
A Declarative Extension of Parsing Expression Grammars for Recognizing Most Programming Languages
Journal of Information Processing Vol.24 No.2 256–264 (Mar. 2016) [DOI: 10.2197/ipsjjip.24.256] Regular Paper A Declarative Extension of Parsing Expression Grammars for Recognizing Most Programming Languages Tetsuro Matsumura1,†1 Kimio Kuramitsu1,a) Received: May 18, 2015, Accepted: November 5, 2015 Abstract: Parsing Expression Grammars are a popular foundation for describing syntax. Unfortunately, several syn- tax of programming languages are still hard to recognize with pure PEGs. Notorious cases appears: typedef-defined names in C/C++, indentation-based code layout in Python, and HERE document in many scripting languages. To recognize such PEG-hard syntax, we have addressed a declarative extension to PEGs. The “declarative” extension means no programmed semantic actions, which are traditionally used to realize the extended parsing behavior. Nez is our extended PEG language, including symbol tables and conditional parsing. This paper demonstrates that the use of Nez Extensions can realize many practical programming languages, such as C, C#, Ruby, and Python, which involve PEG-hard syntax. Keywords: parsing expression grammars, semantic actions, context-sensitive syntax, and case studies on program- ming languages invalidate the declarative property of PEGs, thus resulting in re- 1. Introduction duced reusability of grammars. As a result, many developers need Parsing Expression Grammars [5], or PEGs, are a popu- to redevelop grammars for their software engineering tools. lar foundation for describing programming language syntax [6], In this paper, we propose a declarative extension of PEGs for [15]. Indeed, the formalism of PEGs has many desirable prop- recognizing context-sensitive syntax. The “declarative” exten- erties, including deterministic behaviors, unlimited look-aheads, sion means no arbitrary semantic actions that are written in a gen- and integrated lexical analysis known as scanner-less parsing. -
Efficient Recursive Parsing
Purdue University Purdue e-Pubs Department of Computer Science Technical Reports Department of Computer Science 1976 Efficient Recursive Parsing Christoph M. Hoffmann Purdue University, [email protected] Report Number: 77-215 Hoffmann, Christoph M., "Efficient Recursive Parsing" (1976). Department of Computer Science Technical Reports. Paper 155. https://docs.lib.purdue.edu/cstech/155 This document has been made available through Purdue e-Pubs, a service of the Purdue University Libraries. Please contact [email protected] for additional information. EFFICIENT RECURSIVE PARSING Christoph M. Hoffmann Computer Science Department Purdue University West Lafayette, Indiana 47907 CSD-TR 215 December 1976 Efficient Recursive Parsing Christoph M. Hoffmann Computer Science Department Purdue University- Abstract Algorithms are developed which construct from a given LL(l) grammar a recursive descent parser with as much, recursion resolved by iteration as is possible without introducing auxiliary memory. Unlike other proposed methods in the literature designed to arrive at parsers of this kind, the algorithms do not require extensions of the notational formalism nor alter the grammar in any way. The algorithms constructing the parsers operate in 0(k«s) steps, where s is the size of the grammar, i.e. the sum of the lengths of all productions, and k is a grammar - dependent constant. A speedup of the algorithm is possible which improves the bound to 0(s) for all LL(l) grammars, and constructs smaller parsers with some auxiliary memory in form of parameters -
LLLR Parsing: a Combination of LL and LR Parsing
LLLR Parsing: a Combination of LL and LR Parsing Boštjan Slivnik University of Ljubljana, Faculty of Computer and Information Science, Ljubljana, Slovenia [email protected] Abstract A new parsing method called LLLR parsing is defined and a method for producing LLLR parsers is described. An LLLR parser uses an LL parser as its backbone and parses as much of its input string using LL parsing as possible. To resolve LL conflicts it triggers small embedded LR parsers. An embedded LR parser starts parsing the remaining input and once the LL conflict is resolved, the LR parser produces the left parse of the substring it has just parsed and passes the control back to the backbone LL parser. The LLLR(k) parser can be constructed for any LR(k) grammar. It produces the left parse of the input string without any backtracking and, if used for a syntax-directed translation, it evaluates semantic actions using the top-down strategy just like the canonical LL(k) parser. An LLLR(k) parser is appropriate for grammars where the LL(k) conflicting nonterminals either appear relatively close to the bottom of the derivation trees or produce short substrings. In such cases an LLLR parser can perform a significantly better error recovery than an LR parser since the most part of the input string is parsed with the backbone LL parser. LLLR parsing is similar to LL(∗) parsing except that it (a) uses LR(k) parsers instead of finite automata to resolve the LL(k) conflicts and (b) does not perform any backtracking. -
Parser Generation Bottom-Up Parsing LR Parsing Constructing LR Parser
CS421 COMPILERS AND INTERPRETERS CS421 COMPILERS AND INTERPRETERS Parser Generation Bottom-Up Parsing • Main Problem: given a grammar G, how to build a top-down parser or • Construct parse tree “bottom-up” --- from leaves to the root a bottom-up parser for it ? • Bottom-up parsing always constructs right-most derivation • parser : a program that, given a sentence, reconstructs a derivation for • Important parsing algorithms: shift-reduce, LR parsing that sentence ---- if done sucessfully, it “recognize” the sentence • LR parser components: input, stack (strings of grammar symbols and • all parsers read their input left-to-right, but construct parse tree states), driver routine, parsing tables. differently. input: a1 a2 a3 a4 ......an $ • bottom-up parsers --- construct the tree from leaves to root stack s shift-reduce, LR, SLR, LALR, operator precedence m LR Parsing output Xm • top-down parsers --- construct the tree from root to leaves .... s1 recursive descent, predictive parsing, LL(1) X1 parsing s0 Copyright 1994 - 2000 Carsten Schürmann, Zhong Shao, Yale University Parser Generation: Page 1 of 28 Copyright 1994 - 2000 Carsten Schürmann, Zhong Shao, Yale University Parser Generation: Page 2 of 28 CS421 COMPILERS AND INTERPRETERS CS421 COMPILERS AND INTERPRETERS LR Parsing Constructing LR Parser How to construct the parsing table and ? • A sequence of new state symbols s0,s1,s2,..., sm ----- each state ACTION GOTO sumarize the information contained in the stack below it. • basic idea: first construct DFA to recognize handles, then use DFA to construct the parsing tables ! different parsing table yield different LR • Parsing configurations: (stack, remaining input) written as parsers SLR(1), LR(1), or LALR(1) (s0X1s1X2s2...Xmsm , aiai+1ai+2...an$) • augmented grammar for context-free grammar G = G(T,N,P,S) is next “move” is determined by sm and ai defined as G’ = G’(T,N ??{ S’}, P ??{ S’ -> S}, S’) ------ adding non- • Parsing tables: ACTION[s,a] and GOTO[s,X] terminal S’ and the production S’ -> S, and S’ is the new start symbol. -
Compiler Design
CCOOMMPPIILLEERR DDEESSIIGGNN -- PPAARRSSEERR http://www.tutorialspoint.com/compiler_design/compiler_design_parser.htm Copyright © tutorialspoint.com In the previous chapter, we understood the basic concepts involved in parsing. In this chapter, we will learn the various types of parser construction methods available. Parsing can be defined as top-down or bottom-up based on how the parse-tree is constructed. Top-Down Parsing We have learnt in the last chapter that the top-down parsing technique parses the input, and starts constructing a parse tree from the root node gradually moving down to the leaf nodes. The types of top-down parsing are depicted below: Recursive Descent Parsing Recursive descent is a top-down parsing technique that constructs the parse tree from the top and the input is read from left to right. It uses procedures for every terminal and non-terminal entity. This parsing technique recursively parses the input to make a parse tree, which may or may not require back-tracking. But the grammar associated with it ifnotleftfactored cannot avoid back- tracking. A form of recursive-descent parsing that does not require any back-tracking is known as predictive parsing. This parsing technique is regarded recursive as it uses context-free grammar which is recursive in nature. Back-tracking Top- down parsers start from the root node startsymbol and match the input string against the production rules to replace them ifmatched. To understand this, take the following example of CFG: S → rXd | rZd X → oa | ea Z → ai For an input string: read, a top-down parser, will behave like this: It will start with S from the production rules and will match its yield to the left-most letter of the input, i.e. -
A Simple, Possibly Correct LR Parser for C11 Jacques-Henri Jourdan, François Pottier
A Simple, Possibly Correct LR Parser for C11 Jacques-Henri Jourdan, François Pottier To cite this version: Jacques-Henri Jourdan, François Pottier. A Simple, Possibly Correct LR Parser for C11. ACM Transactions on Programming Languages and Systems (TOPLAS), ACM, 2017, 39 (4), pp.1 - 36. 10.1145/3064848. hal-01633123 HAL Id: hal-01633123 https://hal.archives-ouvertes.fr/hal-01633123 Submitted on 11 Nov 2017 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. 14 A simple, possibly correct LR parser for C11 Jacques-Henri Jourdan, Inria Paris, MPI-SWS François Pottier, Inria Paris The syntax of the C programming language is described in the C11 standard by an ambiguous context-free grammar, accompanied with English prose that describes the concept of “scope” and indicates how certain ambiguous code fragments should be interpreted. Based on these elements, the problem of implementing a compliant C11 parser is not entirely trivial. We review the main sources of difficulty and describe a relatively simple solution to the problem. Our solution employs the well-known technique of combining an LALR(1) parser with a “lexical feedback” mechanism. It draws on folklore knowledge and adds several original as- pects, including: a twist on lexical feedback that allows a smooth interaction with lookahead; a simplified and powerful treatment of scopes; and a few amendments in the grammar. -
Advanced Parsing Techniques
Advanced Parsing Techniques Announcements ● Written Set 1 graded. ● Hard copies available for pickup right now. ● Electronic submissions: feedback returned later today. Where We Are Where We Are Parsing so Far ● We've explored five deterministic parsing algorithms: ● LL(1) ● LR(0) ● SLR(1) ● LALR(1) ● LR(1) ● These algorithms all have their limitations. ● Can we parse arbitrary context-free grammars? Why Parse Arbitrary Grammars? ● They're easier to write. ● Can leave operator precedence and associativity out of the grammar. ● No worries about shift/reduce or FIRST/FOLLOW conflicts. ● If ambiguous, can filter out invalid trees at the end. ● Generate candidate parse trees, then eliminate them when not needed. ● Practical concern for some languages. ● We need to have C and C++ compilers! Questions for Today ● How do you go about parsing ambiguous grammars efficiently? ● How do you produce all possible parse trees? ● What else can we do with a general parser? The Earley Parser Motivation: The Limits of LR ● LR parsers use shift and reduce actions to reduce the input to the start symbol. ● LR parsers cannot deterministically handle shift/reduce or reduce/reduce conflicts. ● However, they can nondeterministically handle these conflicts by guessing which option to choose. ● What if we try all options and see if any of them work? The Earley Parser ● Maintain a collection of Earley items, which are LR(0) items annotated with a start position. ● The item A → α·ω @n means we are working on recognizing A → αω, have seen α, and the start position of the item was the nth token. ● Using techniques similar to LR parsing, try to scan across the input creating these items. -
CLR(1) and LALR(1) Parsers
CLR(1) and LALR(1) Parsers C.Naga Raju B.Tech(CSE),M.Tech(CSE),PhD(CSE),MIEEE,MCSI,MISTE Professor Department of CSE YSR Engineering College of YVU Proddatur 6/21/2020 Prof.C.NagaRaju YSREC of YVU 9949218570 Contents • Limitations of SLR(1) Parser • Introduction to CLR(1) Parser • Animated example • Limitation of CLR(1) • LALR(1) Parser Animated example • GATE Problems and Solutions Prof.C.NagaRaju YSREC of YVU 9949218570 Drawbacks of SLR(1) ❖ The SLR Parser discussed in the earlier class has certain flaws. ❖ 1.On single input, State may be included a Final Item and a Non- Final Item. This may result in a Shift-Reduce Conflict . ❖ 2.A State may be included Two Different Final Items. This might result in a Reduce-Reduce Conflict Prof.C.NagaRaju YSREC of YVU 6/21/2020 9949218570 ❖ 3.SLR(1) Parser reduces only when the next token is in Follow of the left-hand side of the production. ❖ 4.SLR(1) can reduce shift-reduce conflicts but not reduce-reduce conflicts ❖ These two conflicts are reduced by CLR(1) Parser by keeping track of lookahead information in the states of the parser. ❖ This is also called as LR(1) grammar Prof.C.NagaRaju YSREC of YVU 6/21/2020 9949218570 CLR(1) Parser ❖ LR(1) Parser greatly increases the strength of the parser, but also the size of its parse tables. ❖ The LR(1) techniques does not rely on FOLLOW sets, but it keeps the Specific Look-ahead with each item. Prof.C.NagaRaju YSREC of YVU 6/21/2020 9949218570 CLR(1) Parser ❖ LR(1) Parsing configurations have the general form: A –> X1...Xi • Xi+1...Xj , a ❖ The Look Ahead -
Verifiable Parse Table Composition for Deterministic Parsing*
Verifiable Parse Table Composition for Deterministic Parsing? August Schwerdfeger and Eric Van Wyk Department of Computer Science and Engineering University of Minnesota, Minneapolis, MN {schwerdf,evw}@cs.umn.edu Abstract. One obstacle to the implementation of modular extensions to programming languages lies in the problem of parsing extended lan- guages. Specifically, the parse tables at the heart of traditional LALR(1) parsers are so monolithic and tightly constructed that, in the general case, it is impossible to extend them without regenerating them from the source grammar. Current extensible frameworks employ a variety of solutions, ranging from a full regeneration to using pluggable binary mod- ules for each different extension. But recompilation is time-consuming, while the pluggable modules in many cases cannot support the addition of more than one extension, or use backtracking or non-deterministic parsing techniques. We present here a middle-ground approach that allows an extension, if it meets certain restrictions, to be compiled into a parse table fragment. The host language parse table and fragments from multiple extensions can then always be efficiently composed to produce a conflict-free parse table for the extended language. This allows for the distribution of de- terministic parsers for extensible languages in a pre-compiled format, eliminating the need for the “source code” grammar to be distributed. In practice, we have found these restrictions to be reasonable and admit many useful language extensions. 1 Introduction In parsing programming languages, the usual practice is to generate a single parser for the language to be parsed. A well known and often-used approach is LR parsing [1] which relies on a process, sometimes referred to as grammar compilation, to generate a monolithic parse table representing the grammar being parsed. -
Basics of Compiler Design
Basics of Compiler Design Anniversary edition Torben Ægidius Mogensen DEPARTMENT OF COMPUTER SCIENCE UNIVERSITY OF COPENHAGEN Published through lulu.com. c Torben Ægidius Mogensen 2000 – 2010 [email protected] Department of Computer Science University of Copenhagen Universitetsparken 1 DK-2100 Copenhagen DENMARK Book homepage: http://www.diku.dk/∼torbenm/Basics First published 2000 This edition: August 20, 2010 ISBN 978-87-993154-0-6 Contents 1 Introduction 1 1.1 What is a compiler? . 1 1.2 The phases of a compiler . 2 1.3 Interpreters . 3 1.4 Why learn about compilers? . 4 1.5 The structure of this book . 5 1.6 To the lecturer . 6 1.7 Acknowledgements . 7 1.8 Permission to use . 7 2 Lexical Analysis 9 2.1 Introduction . 9 2.2 Regular expressions . 10 2.2.1 Shorthands . 13 2.2.2 Examples . 14 2.3 Nondeterministic finite automata . 15 2.4 Converting a regular expression to an NFA . 18 2.4.1 Optimisations . 20 2.5 Deterministic finite automata . 22 2.6 Converting an NFA to a DFA . 23 2.6.1 Solving set equations . 23 2.6.2 The subset construction . 26 2.7 Size versus speed . 29 2.8 Minimisation of DFAs . 30 2.8.1 Example . 32 2.8.2 Dead states . 34 2.9 Lexers and lexer generators . 35 2.9.1 Lexer generators . 41 2.10 Properties of regular languages . 42 2.10.1 Relative expressive power . 42 2.10.2 Limits to expressive power . 44 i ii CONTENTS 2.10.3 Closure properties . 45 2.11 Further reading .