DOCSLIB.ORG

Chapter 5 Disambiguation Strategies

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Chapter 5 Disambiguation Strategies Eindhoven University of Technology MASTER Disambiguation mechanisms and disambiguation strategies ten Brink, A.P. Award date: 2013 Link to publication Disclaimer This document contains a student thesis (bachelor's or master's), as authored by a student at Eindhoven University of Technology. Student theses are made available in the TU/e repository upon obtaining the required degree. The grade received is not published on the document as presented in the repository. The required complexity or quality of research of student theses may vary by program, and the required minimum study period may vary in duration. General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain Department of Mathematics and Computer Science Den Dolech 2, 5612 AZ Eindhoven P.O. Box 513, 5600 MB Eindhoven The Netherlands www.tue.nl Supervisor prof. dr. Mark van den Brand (TU/e) Disambiguation mechanisms and Section disambiguation strategies Model Driven Software Engineering Date August 23, 2013 Master’s Thesis Alex P. ten Brink Where innovation starts Abstract It is common practice for engineers to express artifacts such as domain specific languages using ambiguous context-free grammars along with separate disambiguation rules, rather than to use equivalent unambiguous context-free grammars, as these ambiguous grammars often capture the intent of the engineer more directly. In this thesis we give an overview of the landscape of these disambiguation rules as described in the literature. In order to make proper comparisons between publications, we define a parser-technology independent framework for describing work done on disambiguation, using the notions of a disambiguation mechanism and a disambiguation strategy. We investigate for a number of disambiguation mechanisms what grammars they can dis- ambiguate, both relative to each other and in absolute terms: for example, we prove that the LR shift-reduce conflict resolution mechanism (used in the well-known tools Yacc and Bison) correctly disambiguates expression grammars with unary and binary operators. To the best of our knowledge, previous work only considered the correctness of this mechanism on specific grammars. We define six quality measures on disambiguation strategies and compare the strategies intro- duced in the literature to each other on these measures. Finally, we introduce our own strategy based on regular rewriting rules. Our strategy scores well on all but one of our quality mea- sures. Its weakness is that it may not succeed on all grammars. Our strategy is very extensible however, and we give multiple options to improve its applicability in future work. iii Acknowledgements I would first like to thank my parents for their unconditional support, not only while I was working on my thesis but throughout my academic career (and before that) – I would not have gotten to where I am without them. I would also like to thank the friends I made over the past five years for making my student life fun rather than a chore. In particular I thank Sander and Quirijn, who made even the contents of courses fun to work through, and Ana, for making the time I spent at the TU working on this thesis that much more enjoyable. I would like to thank Mark van den Brand for his guidance, feedback and insights, both on this thesis and on related work. The other major influence on this thesis and related work is Elisabeth Scott, whom I’d like to thank for her feedback, ideas and time, and in particular her remarks whenever I was not being precise enough – though I fear I may get a few more still. I would also like to thank Adrian Johnstone for reviewing part of this thesis. Finally, I would like to thank Tom Verhoeff and Kevin Buchin for serving on my examination committee. Alex P. ten Brink Eindhoven August 23, 2013 v Contents Abstract iii Acknowledgements v 1 Introduction 1 2 Preliminaries 5 3 Disambiguation Mechanisms 9 3.1Introduction....................................... 9 3.2DisambiguationMechanismsIntroducedintheLiterature............. 10 3.3ComparisonofDisambiguationMechanisms..................... 13 4 Applying Disambiguation Mechanisms to Expression Grammars 17 4.1Real-WorldExampleofaHardDisambiguationProblem.............. 17 4.2 Expression Grammars . .............................. 20 4.3 Defining Precedences and Associativities ....................... 21 4.4DisambiguatingExpressionGrammarswithBinaryOperators........... 23 4.5AddingUnaryOperators................................ 25 5 Disambiguation Strategies 29 5.1Introduction....................................... 29 5.2DisambiguationStrategiesIntroducedintheLiterature............... 30 6 A rewriting-based disambiguation strategy 35 6.1Introduction....................................... 35 6.2 Systems of Regular Expressions ............................ 38 6.3 Rewriting Rules for Regular Expressions ....................... 38 6.3.1 DefiningRewriters............................... 38 6.3.2 OurRewriters................................. 39 6.4StructuredRegularExpressions............................ 42 6.5RewritingRulesforStructuredRegularExpressions................ 44 vii 6.5.1 DefiningRewriters............................... 44 6.5.2 Enforcing Filters ................................ 44 6.5.3 OurRewriters................................. 45 7 Implementation 49 8 Conclusion 53 viii Chapter 1 Introduction Context-free grammars are widely-used tools for describing the syntactic structure of program- ming languages. They are often used to describe the second step in the analysis of an input program, after (optional) tokenization and before semantic analysis. An advantage of this de- scription is that for any context-free grammar it is possible to automatically generate a program, called a parser, that can compute this syntactic structure from an input, represented as a parse tree. A disadvantage is that some context-free grammars contain ambiguities: a single input may be parsed to multiple parse trees. For programming languages it is desirable that a program has only one interpretation. There- fore one would like to only use unambiguous context-free grammars. However, in some cases the most natural context-free description of a programming language is an ambiguous one. An example are arithmetic expressions: the most natural grammar for them leaves open whether 1+2∗ 3 should be parsed as (1 + 2) ∗ 3oras1+(2∗ 3). Some additional form of disambiguation is then needed: in our example we would need to specify whether ∗ has priority over +. Often, an unambiguous grammar capturing this additional disambiguation can be found, but these grammars are often much larger, less intuitive and less maintainable as they may not capture the intent of the language designer. Mechanisms and Strategies In practice it is therefore often preferable to use a syntactic description with two parts: an ambiguous context-free grammar along with disambiguation rules. There are many ways to achieve this separation, and many such methods have been described in the literature. We call everything involved in such a method – the allowed disambiguation rules, the way these disambiguation rules are applied in the parser, the way the language designer is helped with choosing these rules and with understanding the effects of the rules – a disambiguation strategy. Most strategies have at their core a specific mechanism that specifies how inputs should be parsed (or in some cases, multiple mechanisms). Strategies (usually) detect ambiguities in the grammar and then find the right parameters for the mechanisms they employ so the inputs are parsed as specified. As a number of such disambiguation mechanisms are very common, it is useful to analyze them separately from the strategies that use them. When we are investigating strategies, we can use this knowledge of the mechanisms it uses to immediately say something about the strategy. As an example of a strategy, consider the disambiguation strategy typical for users of Yacc or Bison. First, they create an initial grammar, generate the LR table and analyze the resulting conflicts by hand. They then specify precedence and associativity rules for their grammar that 1 Yacc or Bison then uses to resolve the conflicts in the table. The disambiguation mechanism involved in this strategy is the resolution of LR table conflicts. Note that this strategy is not really a good strategy as the user needs to do most of the work himself and the strategy may fail to work as not all ambiguities can be resolved through LR table conflict resolution. While many disambiguation mechanisms and strategies have been proposed, not much work has gone into their evaluation and comparison. For example, to the best of our knowledge the correctness of the mechanism employed by Yacc and Bison has only been considered on examples. This thesis sets some initial steps in this area by defining our parser-technology inde- pendent framework consisting of the notions of disambiguation mechanisms and disambiguation strategies and by addressing the following questions: • What are relevant quality criteria for disambiguation mechanisms? • Which disambiguation
Load more

Recommended publications
  • Adaptive LL(*) Parsing: the Power of Dynamic Analysis
    Adaptive LL(*) Parsing: The Power of Dynamic Analysis Terence Parr Sam Harwell Kathleen Fisher University of San Francisco University of Texas at Austin Tufts University [email protected] [email protected] kfi[email protected] Abstract PEGs are unambiguous by definition but have a quirk where Despite the advances made by modern parsing strategies such rule A ! a j ab (meaning “A matches either a or ab”) can never as PEG, LL(*), GLR, and GLL, parsing is not a solved prob- match ab since PEGs choose the first alternative that matches lem. Existing approaches suffer from a number of weaknesses, a prefix of the remaining input. Nested backtracking makes de- including difficulties supporting side-effecting embedded ac- bugging PEGs difficult. tions, slow and/or unpredictable performance, and counter- Second, side-effecting programmer-supplied actions (muta- intuitive matching strategies. This paper introduces the ALL(*) tors) like print statements should be avoided in any strategy that parsing strategy that combines the simplicity, efficiency, and continuously speculates (PEG) or supports multiple interpreta- predictability of conventional top-down LL(k) parsers with the tions of the input (GLL and GLR) because such actions may power of a GLR-like mechanism to make parsing decisions. never really take place [17]. (Though DParser [24] supports The critical innovation is to move grammar analysis to parse- “final” actions when the programmer is certain a reduction is time, which lets ALL(*) handle any non-left-recursive context- part of an unambiguous final parse.) Without side effects, ac- free grammar. ALL(*) is O(n4) in theory but consistently per- tions must buffer data for all interpretations in immutable data forms linearly on grammars used in practice, outperforming structures or provide undo actions.
    [Show full text]
  • Disambiguation Filters for Scannerless Generalized LR Parsers
    Disambiguation Filters for Scannerless Generalized LR Parsers Mark G. J. van den Brand1,4, Jeroen Scheerder2, Jurgen J. Vinju1, and Eelco Visser3 1 Centrum voor Wiskunde en Informatica (CWI) Kruislaan 413, 1098 SJ Amsterdam, The Netherlands {Mark.van.den.Brand,Jurgen.Vinju}@cwi.nl 2 Department of Philosophy, Utrecht University Heidelberglaan 8, 3584 CS Utrecht, The Netherlands [email protected] 3 Institute of Information and Computing Sciences, Utrecht University P.O. Box 80089, 3508TB Utrecht, The Netherlands [email protected] 4 LORIA-INRIA 615 rue du Jardin Botanique, BP 101, F-54602 Villers-l`es-Nancy Cedex, France Abstract. In this paper we present the fusion of generalized LR pars- ing and scannerless parsing. This combination supports syntax defini- tions in which all aspects (lexical and context-free) of the syntax of a language are defined explicitly in one formalism. Furthermore, there are no restrictions on the class of grammars, thus allowing a natural syntax tree structure. Ambiguities that arise through the use of unrestricted grammars are handled by explicit disambiguation constructs, instead of implicit defaults that are taken by traditional scanner and parser gener- ators. Hence, a syntax definition becomes a full declarative description of a language. Scannerless generalized LR parsing is a viable technique that has been applied in various industrial and academic projects. 1 Introduction Since the introduction of efficient deterministic parsing techniques, parsing is considered a closed topic for research, both by computer scientists and by practi- cioners in compiler construction. Tools based on deterministic parsing algorithms such as LEX & YACC [15,11] (LALR) and JavaCC (recursive descent), are con- sidered adequate for dealing with almost all modern (programming) languages.
    [Show full text]
  • Introduction to Programming Languages and Syntax
    Programming Languages Session 1 – Main Theme Programming Languages Overview & Syntax Dr. Jean-Claude Franchitti New York University Computer Science Department Courant Institute of Mathematical Sciences Adapted from course textbook resources Programming Language Pragmatics (3rd Edition) Michael L. Scott, Copyright © 2009 Elsevier 1 Agenda 11 InstructorInstructor andand CourseCourse IntroductionIntroduction 22 IntroductionIntroduction toto ProgrammingProgramming LanguagesLanguages 33 ProgrammingProgramming LanguageLanguage SyntaxSyntax 44 ConclusionConclusion 2 Who am I? - Profile - ¾ 27 years of experience in the Information Technology Industry, including thirteen years of experience working for leading IT consulting firms such as Computer Sciences Corporation ¾ PhD in Computer Science from University of Colorado at Boulder ¾ Past CEO and CTO ¾ Held senior management and technical leadership roles in many large IT Strategy and Modernization projects for fortune 500 corporations in the insurance, banking, investment banking, pharmaceutical, retail, and information management industries ¾ Contributed to several high-profile ARPA and NSF research projects ¾ Played an active role as a member of the OMG, ODMG, and X3H2 standards committees and as a Professor of Computer Science at Columbia initially and New York University since 1997 ¾ Proven record of delivering business solutions on time and on budget ¾ Original designer and developer of jcrew.com and the suite of products now known as IBM InfoSphere DataStage ¾ Creator of the Enterprise
    [Show full text]
  • Packrat Parsing: Simple, Powerful, Lazy, Linear Time
    Packrat Parsing: Simple, Powerful, Lazy, Linear Time Functional Pearl Bryan Ford Massachusetts Institute of Technology Cambridge, MA [email protected] Abstract 1 Introduction Packrat parsing is a novel technique for implementing parsers in a There are many ways to implement a parser in a functional pro- lazy functional programming language. A packrat parser provides gramming language. The simplest and most direct approach is the power and flexibility of top-down parsing with backtracking and top-down or recursive descent parsing, in which the components unlimited lookahead, but nevertheless guarantees linear parse time. of a language grammar are translated more-or-less directly into a Any language defined by an LL(k) or LR(k) grammar can be rec- set of mutually recursive functions. Top-down parsers can in turn ognized by a packrat parser, in addition to many languages that be divided into two categories. Predictive parsers attempt to pre- conventional linear-time algorithms do not support. This additional dict what type of language construct to expect at a given point by power simplifies the handling of common syntactic idioms such as “looking ahead” a limited number of symbols in the input stream. the widespread but troublesome longest-match rule, enables the use Backtracking parsers instead make decisions speculatively by try- of sophisticated disambiguation strategies such as syntactic and se- ing different alternatives in succession: if one alternative fails to mantic predicates, provides better grammar composition properties, match, then the parser “backtracks” to the original input position and allows lexical analysis to be integrated seamlessly into parsing. and tries another.
    [Show full text]
  • Compiler Construction
    Compiler construction PDF generated using the open source mwlib toolkit. See http://code.pediapress.com/ for more information. PDF generated at: Sat, 10 Dec 2011 02:23:02 UTC Contents Articles Introduction 1 Compiler construction 1 Compiler 2 Interpreter 10 History of compiler writing 14 Lexical analysis 22 Lexical analysis 22 Regular expression 26 Regular expression examples 37 Finite-state machine 41 Preprocessor 51 Syntactic analysis 54 Parsing 54 Lookahead 58 Symbol table 61 Abstract syntax 63 Abstract syntax tree 64 Context-free grammar 65 Terminal and nonterminal symbols 77 Left recursion 79 Backus–Naur Form 83 Extended Backus–Naur Form 86 TBNF 91 Top-down parsing 91 Recursive descent parser 93 Tail recursive parser 98 Parsing expression grammar 100 LL parser 106 LR parser 114 Parsing table 123 Simple LR parser 125 Canonical LR parser 127 GLR parser 129 LALR parser 130 Recursive ascent parser 133 Parser combinator 140 Bottom-up parsing 143 Chomsky normal form 148 CYK algorithm 150 Simple precedence grammar 153 Simple precedence parser 154 Operator-precedence grammar 156 Operator-precedence parser 159 Shunting-yard algorithm 163 Chart parser 173 Earley parser 174 The lexer hack 178 Scannerless parsing 180 Semantic analysis 182 Attribute grammar 182 L-attributed grammar 184 LR-attributed grammar 185 S-attributed grammar 185 ECLR-attributed grammar 186 Intermediate language 186 Control flow graph 188 Basic block 190 Call graph 192 Data-flow analysis 195 Use-define chain 201 Live variable analysis 204 Reaching definition 206 Three address
    [Show full text]
  • Incremental Scannerless Generalized LR Parsing
    SPLASH: G: Incremental Scannerless Generalized LR Parsing Maarten P. Sijm Delft University of Technology Delft, The Netherlands [email protected] Abstract a separate lexing (or scanning) phase, supports modelling We present the Incremental Scannerless Generalized LR the entire language syntax in one single grammar, and al- (ISGLR) parsing algorithm, which combines the benefits of lows composition of grammars for different languages. One Incremental Generalized LR (IGLR) parsing and Scanner- notable disadvantage is that the SGLR parsing algorithm of less Generalized LR (SGLR) parsing. The ISGLR parser can Visser [9] is a batch algorithm, meaning that it must process reuse parse trees from unchanged regions in the input and each entire file in one pass. This becomes a problem for soft- thus only needs to parse changed regions. We also present ware projects that have large files, as every small change incremental techniques for imploding the parse tree to an requires the entire file to be parsed again. Abstract Syntax Tree (AST) and syntax highlighting. Scan- Incremental Generalized LR (IGLR) parsing is an improve- nerless parsing relies heavily on non-determinism during ment over batch Generalized LR (GLR) parsing. Amongst parsing, negatively impacting the incrementality of ISGLR others, Wagner [10] and TreeSitter [8] have created parsing parsing. We evaluated the ISGLR parsing algorithm using algorithms that allow rapid parsing of changes to large files. file histories from Git, achieving a speedup of up to 25 times However, these algorithms use a separate incremental lexical over non-incremental SGLR. analysis phase which complicates the implementation of in- cremental parsing [11] and does not directly allow language CCS Concepts • Software and its engineering → Incre- composition.
    [Show full text]
  • Faster Scannerless GLR Parsing
    Faster Scannerless GLR Parsing Giorgios Economopoulos, Paul Klint, Jurgen Vinju Centrum voor Wiskunde en Informatica (CWI), Kruislaan 413, 1098 SJ Amsterdam, The Netherlands Abstract. Analysis and renovation of large software portfolios requires syntax analysis of multiple, usually embedded, languages and this is be- yond the capabilities of many standard parsing techniques. The tradi- tional separation between lexer and parser falls short due to the limita- tions of tokenization based on regular expressions when handling multiple lexical grammars. In such cases scannerless parsing provides a viable so- lution. It uses the power of context-free grammars to be able to deal with a wide variety of issues in parsing lexical syntax. However, it comes at the price of less efficiency. The structure of tokens is obtained using a more powerful but more time and memory intensive parsing algorithm. Scan- nerless grammars are also more non-deterministic than their tokenized counterparts, increasing the burden on the parsing algorithm even fur- ther. In this paper we investigate the application of the Right-Nulled Gener- alized LR parsing algorithm (RNGLR) to scannerless parsing. We adapt the Scannerless Generalized LR parsing and filtering algorithm (SGLR) to implement the optimizations of RNGLR. We present an updated pars- ing and filtering algorithm, called SRNGLR, and analyze its performance in comparison to SGLR on ambiguous grammars for the programming languages C, Java, Python, SASL, and C++. Measurements show that SRNGLR is on average 33% faster than SGLR, but is 95% faster on the highly ambiguous SASL grammar. For the mainstream languages C, C++, Java and Python the average speedup is 16%.
    [Show full text]
  • Mining Structured Data in Natural Language Artifacts with Island Parsing
    Science of Computer Programming 150 (2017) 31–55 Contents lists available at ScienceDirect Science of Computer Programming www.elsevier.com/locate/scico Mining structured data in natural language artifacts with island parsing ∗ Alberto Bacchelli a, , Andrea Mocci b, Anthony Cleve c, Michele Lanza b a Department of Informatics, University of Zurich, Switzerland b REVEAL @ Software Institute, Università della Svizzera italiana (USI), Switzerland c Faculty of Informatics, University of Namur, Belgium a r t i c l e i n f o a b s t r a c t Article history: Software repositories typically store data composed of structured and unstructured Received 11 December 2015 parts. Researchers mine this data to empirically validate research ideas and to support Received in revised form 18 November 2016 practitioners’ activities. Structured data (e.g., source code) has a formal syntax and is Accepted 13 June 2017 straightforward to analyze; unstructured data (e.g., documentation) is a mix of natural Available online 1 August 2017 language, noise, and snippets of structured data, and it is harder to analyze. Especially Keywords: the structured content (e.g., code snippets) in unstructured data contains valuable Mining software repositories information. Unstructured data Researchers have proposed several approaches to recognize, extract, and analyze structured Island parsing data embedded in natural language. We analyze these approaches and investigate their drawbacks. Subsequently, we present two novel methods, based on scannerless generalized LR (SGLR) and Parsing Expression Grammars (PEGs), to address these drawbacks and to mine structured fragments within unstructured data. We validate and compare these approaches on development emails and Stack Overflow posts with Java code fragments.
    [Show full text]
  • LNCS 9031, Pp
    Faster, Practical GLL Parsing Ali Afroozeh and Anastasia Izmaylova Centrum Wiskunde & Informatica, 1098 XG Amsterdam, The Netherlands {ali.afroozeh, anastasia.izmaylova}@cwi.nl Abstract. Generalized LL (GLL) parsing is an extension of recursive- descent (RD) parsing that supports all context-free grammars in cubic time and space. GLL parsers have the direct relationship with the gram- mar 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. In this paper we propose a more efficient Graph-Structured Stack (GSS) for GLL parsing that leads to significant performance improve- ment. We also discuss a number of optimizations that further improve the performance of GLL. Finally, for practical scannerless parsing of pro- gramming languages, we show how common lexical disambiguation filters canbeintegratedinGLLparsing. Our new formulation of GLL parsing is implemented as part of the Iguana parsing framework. We evaluate the effectiveness of our approach using a highly-ambiguous grammar and grammars of real programming languages. Our results, compared to the original GLL, show a speedup factor of 10 on the highly-ambiguous grammar, and a speedup factor of 1.5, 1.7, and 5.2 on the grammars of Java, C#, and OCaml, respectively. 1 Introduction Developing efficient parsers for programming languages is a difficult task that is usually automated by a parser generator. Since Knuth’s seminal paper [1] on LR parsing, and DeRemer’s work on practical LR parsing (LALR) [2], parsers of many major programming languages have been constructed using LALR parser generators such as Yacc [3].
    [Show full text]
  • One Parser to Rule Them All
    One Parser to Rule Them All Ali Afroozeh Anastasia Izmaylova Centrum Wiskunde & Informatica, Amsterdam, The Netherlands {ali.afroozeh, anastasia.izmaylova}@cwi.nl Abstract 1. Introduction Despite the long history of research in parsing, constructing Parsing is a well-researched topic in computer science, and parsers for real programming languages remains a difficult it is common to hear from fellow researchers in the field and painful task. In the last decades, different parser gen- of programming languages that parsing is a solved prob- erators emerged to allow the construction of parsers from a lem. This statement mostly originates from the success of BNF-like specification. However, still today, many parsers Yacc [18] and its underlying theory that has been developed are handwritten, or are only partly generated, and include in the 70s. Since Knuth’s seminal paper on LR parsing [25], various hacks to deal with different peculiarities in program- and DeRemer’s work on practical LR parsing (LALR) [6], ming languages. The main problem is that current declara- there is a linear parsing technique that covers most syntactic tive syntax definition techniques are based on pure context- constructs in programming languages. Yacc, and its various free grammars, while many constructs found in program- ports to other languages, enabled the generation of efficient ming languages require context information. parsers from a BNF specification. Still, research papers and In this paper we propose a parsing framework that em- tools on parsing in the last four decades show an ongoing braces context information in its core. Our framework is effort to develop new parsing techniques.
    [Show full text]
  • Semantics and Algorithms for Data-Dependent Grammars
    Semantics and Algorithms for Data-dependent Grammars Trevor Jim Yitzhak Mandelbaum David Walker AT&T Labs-Research AT&T Labs-Research Princeton University [email protected] [email protected] [email protected] Abstract ern times, programmer convenience and expressiveness were sacri- Traditional parser generation technologies are incapable of han- ficed for performance. Since then, for the most part, PL researchers dling the demands of modern programmers. In this paper, we have hung on to legacy tools because they are well-known, well- supported, taught in school and universally available, rather than present the design and theory of a new parsing engine, YAKKER, capable of handling the requirements of modern applications in- because they are optimally designed. cluding (1) full scannerless context-free grammars with (2) reg- On the other hand, programmers outside the minuscule world ular expressions as right-hand sides for defining nonterminals. of PL implementors almost never use parser generators. Despite the fact that they are constantly writing parsers—for data formats, YAKKER also includes (3) facilities for binding variables to in- termediate parse results and (4) using such bindings within arbi- networking protocols, configuration files, web scraping and small trary constraints to control parsing. These facilities allow the kind domain-specific languages—they do most of it by hand, often us- of data-dependent parsing commonly needed in systems applica- ing regular expression libraries that support context-sensitive fea- tions, particularly those that operate over binary data. In addition, tures like backreferencing and POSIX anchors. This is not because (5) nonterminals may be parameterized by arbitrary values, which they are unaware of PL tools, but rather because these tools do not gives the system good modularity and abstraction properties in the solve their problems.
    [Show full text]
  • 10. Pegs, Packrats and Parser Combinators
    10. PEGs, Packrats and Parser Combinators! Prof. O. Nierstrasz! Thanks to Bryan Ford for his kind permission to reuse and adapt the slides of his POPL 2004 presentation on PEGs.! http://www.brynosaurus.com/! PEGs, Packrat Parsers and Scannerless Parsing! Roadmap! >" Domain Specific Languages! >" Parsing Expression Grammars! >" Packrat Parsers! >" Parser Combinators! © Oscar Nierstrasz! 2 PEGs, Packrat Parsers and Scannerless Parsing! Sources! >" Parsing Techniques — A Practical Guide! —" Grune & Jacobs, Springer, 2008! —" [Chapter 15.7 — Recognition Systems]" >" “Parsing expression grammars: a recognition-based syntactic foundation” ! —" Ford, POPL 2004, doi:10.1145/964001.964011! >" “Packrat parsing: simple, powerful, lazy, linear time”! —" Ford, ICFP 02, doi:10.1145/583852.581483! >" The Packrat Parsing and Parsing Expression Grammars Page:! —" http://pdos.csail.mit.edu/~baford/packrat/! >" Dynamic Language Embedding With Homogeneous Tool Support! —" Renggli, PhD thesis, 2010, http://scg.unibe.ch/bib/Reng10d! © Oscar Nierstrasz! 3! PEGs, Packrat Parsers and Scannerless Parsing! Roadmap! >" Domain Specific Languages! >" Parsing Expression Grammars! >" Packrat Parsers! >" Parser Combinators! © Oscar Nierstrasz! 4 PEGs, Packrat Parsers and Scannerless Parsing! Domain Specific Languages! >" A DSL is a specialized language targeted to a particular problem domain! —" Not a GPL! —" May be internal or external to a host GPL! —" Examples: SQL, HTML, Makefiles! © Oscar Nierstrasz! 5 PEGs, Packrat Parsers and Scannerless Parsing! Internal DSLs! A “Fluent
    [Show full text]