Parsing Tutorial

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parsing Getting started with parsing – RIP Tutorial

Parsing, in common usage, refers to analysing a piece of language, such as a sentence, and using the grammar rules of that language to identify the components pieces and thus learn the meaning. In computer science it refers to a specific algorithmic process of recognising the sequence of symbols as a valid one, and permit the meaning (or semantics) of a language construct to be determined, often in a computer language compiler or interpreter.
A simple parser
The simplest way to write a parser is to use the recursive descent technique. This creates a top-down parser (which may formally be described a LL(1)). To start the example we first have to establish the grammar rules for our language. In this example we will use simple arithmetic expression assignments for expressions that can only use the plus operator:
Assignment –> Identifier:= Expression
Expression –> Expression + Term | Term
Term –> Identifier | (Expression)
Identifier –> x | y | z
For each rule of the grammar we can write a procedure to recognise the incoming tokens to the rule. For the purposes of this example we can assume a routine called NextToken which invokes a lexical analyser to supply the token, and a routine called error which is used to output an error message. The language used is based on Pascal.
var token:char; (* Updated by NextToken *)
procedure identifier;
begin
if token in [‘x’, ‘y’, ‘z’]
then
NextToken
else
error(‘Identifier expected’)
end;
You can see that this code implements the rule for recognising an Identifier. We can then implement the rule for a Term similarly:
procedure expression forward;
procedure term;
if token = ‘(‘
nextToken;
expression;
if token <> ‘)’
error(‘) expected’)
else NextToken
end
identifier
When we come to implement the rule for Expression we have a problem; the first element of the Expression rule is itself an Expression. This would cause us to write the following:
procedure expression;
expression;…
This is directly self-recursive and thus would loop forever. Grammar parsed by top-down algorithms cannot be left-recursive for this reason. An easy way out of this problem is to recast the recursion as iteration in the following way:
Expression –> Term { + Term}*
We can now code up the grammar rule as:
term;
while token = ‘+’
do
NextTerm;
term
We can now complete the parser with the remaining rule for the assignment:
procedure assignment;
identifier;
if token <> ‘=’
error(‘= expected’)
NextToken;
Example of Parsing an English sentence
For example, in the sentence:
That cake is extremely nice.
The rules of the English language would make cake a noun, extremely an adverb that modifies the adjective nice, and through this analysis the meaning could be understood.
However, this analysis is dependent on us recognising that the sequence of symbols used are words. If the characters used were not familiar to us we would not be able to do this. If we encountered a sentence using an unfamiliar notation, such as Chinese, parsing in this manner might be difficult. Here is an example Chinese sentence:
我可以写一点儿中文。
For anyone who does not read Chinese characters, it would not be clear which symbols combined to form words. The same could be true for a computer algorithm when processing either English or Chinese.
Thus parsing must be proceeded by a process known as lexical analysis or scanning, where the individual characters are grouped together into recognised symbols, which we might commonly call words, but in parsing algorithms are called tokens.
What you need for parsing
In performing parsing, before starting, the grammar for the language needs to be specified. A source of tokens is also needed for the parser.
The parser could be hand-written code, or a parser generator tool could be used. If a parser generator tool is used, then that tool will need to be downloaded and installed if it has not already been included in your platform.
Grammar definitions
A grammar for a parser would normally need to be written in a context free form. A notation like BNF (Backus-Naur Form) or EBNF (Extended Back-Naur Form) is often used for this. Other notations commonly used to describe programming languages might be railroad diagrams.
Lexical Analysis
Tokens are normally provided for the parser by a lexical analyser (or scanner). More details can be found in the documentation for a lexical analyser (TBC).
Parsing Techniques
To hand-code a parser, an appropriate algorithm would need to be chosen that suits both the language been parsed and the means of implementation. Parsing algorithms are classified into the two types of top-down parsing and bottom-up parsing. A (recursive) top-down parser is easier for a beginner to learn when starting to write parsers.
Parser Generator Tools
The most common way of creating a parser is to use a parser generator tool. There are many such tools, but some of the most commonly used are:
Bison/yacc
ANTLR
A Guide To Parsing: Algorithms And Terminology - Federico ...

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A Guide To Parsing: Algorithms And Terminology – Federico …

We have already introduced a few parsing terms, while listing the major tools and libraries used for parsing in Java, C#, Python and JavaScript. In this article we make a more in-depth presentation of the concepts and algorithms used in parsing, so that you can get a better understanding of this fascinating world.
We have tried to be practical in this article. Our goal is to help practitioners, not to explain the full theory. We just explain what you need to know to understand and build a parser.
After the definition of parsing the article is divided in three parts:
The Big Picture. A section in which we describe the fundamental terms and components of a ammars. In this part we explain the main formats of a grammar and the most common issues in writing rsing Algorithms. Here we discuss all the most used parsing algorithms and say what they are good for.
A Guide to Parsing as a PDF Get the Guide to Parsing delivered to your email and read it when you want on the device you want
Definition of Parsing
The analysis of an input to organize the data according to the rule of a grammar
There are a few ways to define the meaning of parsing. However the gist remain the same: parsing means to find the underlying structure of the data we are given.
In a way parsing can be considered the inverse of templating: identifying the structure and extracting the data. In templating we have a structure and we fill it with data instead. In the case of parsing you have to determine the model from the raw representation. While for templating you have to combine the data with the model, to create the raw representation. Raw representation is usually text, but it can also be binary data.
Fundamentally parsing is necessary because different entities need the data to be in different forms. Parsing allows to transform data in a way that can be understood by a specific software. The obvious example are programs: they are written by humans, but they must be executed by computers. So humans write them in a form that they can understand, then software transforms them in a way that can be used by a computer.
However parsing might be necessary even when passing data between two software that have different needs. For instance, it is needed when you have to serialize or deserialize a class.
The Big Picture
In this section we are going to describe the fundamental components of a parser. We are not trying to give you formal explanations, but practical ones.
Regular Expressions
A sequence of characters that can be defined by a pattern
Regular expression are often touted as the thing you should not use for parsing. This is not strictly correct, because you can use regular expressions for parsing simple input. The problem is that some programmers only know regular expressions. So they use them to try to parse everything, even the things they should not. The result is usually a series of regular expressions hacked together, that are very fragile.
You can use regular expressions to parse some simpler languages, but this excludes most programming languages, even the ones that look simple enough like HTML. In fact, languages that can be parsed with just regular expressions are called regular languages. There is a formal mathematical definition, but that is beyond the scope of this article.
Though one important consequence of the theory is that regular languages can be parsed or expressed also by a finite state machine. That is to say regular expressions and finite state machines are equally powerful. This is the reason why they are used to implement lexers, as we are going to see later.
A regular language can be defined by a series of regular expressions, while more complex languages need something more. A simple rule of thumb is: if a grammar of a language has recursive, or nested, elements it is not a regular language. For instance, HTML can contain an arbitrary number of tags inside any tag, therefore is not a regular language and it cannot be parsed using solely regular expressions, no matter how clever.
Regular Expressions in Grammars
The familiarity of a typical programmer with regular expressions often lends them to be used to define the grammar of a language. More precisely, their syntax is used to define the rules of a lexer or a parser. For example, the Kleene star (*) is used in a rule to indicate that a particular element can be present zero or an infinite amount of times.
The definition of the rule should not be confused with how the actual lexer or parser is implemented. You can implement a lexer using the regular expression engine provided by your language. However usually the regular expressions defined in the grammar are actually converted to a finite-state machine to gain better performance.
Structure of a Parser
Having now clarified the role of regular expressions, we can look at the general structure of a parser. A complete parser is usually composed of two parts: a lexer, also known as scanner or tokenizer, and the proper parser. The parser needs the lexer because it does not work directly on the text, but on the output produced by the lexer. Not all parsers adopt this two-steps schema: some parsers do not depend on a separate lexer and they combine the two steps. They are called scannerless parsers.
A lexer and a parser work in sequence: the lexer scans the input and produces the matching tokens, the parser then scans the tokens and produces the parsing result.
Let’s look at the following example and imagine that we are trying to parse an addition.
437 + 734
The lexer scans the text and finds 4, 3, 7 and then a space (). The job of the lexer is to recognize that the characters 437 constitute one token of type NUM. Then the lexer finds a + symbol, which corresponds to a second token of type PLUS, and lastly it finds another token of type NUM.
The parser will typically combine the tokens produced by the lexer and group them.
The input stream is transformed into tokens and then into an AST by the parser
The definitions used by lexers and parsers are called rules or productions. In our example a lexer rule will specify that a sequence of digits corresponds to a token of type NUM, while a parser rule will specify that a sequence of tokens of type NUM, PLUS, NUM corresponds to a sum expression.
It is now typical to find suites that can generate both a lexer and parser. In the past it was instead more common to combine two different tools: one to produce the lexer and one to produce the parser. For example, this was the case of the venerable lex & yacc couple: using lex it was possible to generate a lexer, while using yacc it was possible to generate a parser.
Scannerless Parsers
Scannerless parsers operate differently because they process directly the original text, instead of processing a list of tokens produced by a lexer. That is to say, a scannerless parser works as a lexer and a parser combined.
While it is certainly important to know for debugging purposes if a particular parsing tool is a scannerless parser or not, in many cases it is not relevant to defining a grammar. That is because you usually still define patterns that group sequence of characters to create (virtual) tokens; then you combine them to obtain the final result. This is simply because it is more convenient to do so.
In other words, the grammar of a scannerless parser looks very similar to one for a tool with separate steps. Again, you should not confuse how things are defined for your convenience and how things work behind the scene.
Grammar
A formal grammar is a set of rules that syntactically describes a language
There are two important parts in this definition: a grammar describes a language, but this description pertains only to the syntax of the language and not the semantics. That is to say, it defines its structure, but not its meaning. The correctness of the meaning of the input must be checked, if necessary, in some other way.
For instance, imagine that we want to define a grammar for the language shown in the paragraph Definition of Parsing.
HELLO: “Hello”
NAME: [a-zA-Z]+
greeting: HELLO NAME
This grammar accepts input such as “Hello Michael” and “Hello Programming”. They are both syntactically correct, but we know that Programming it is not a name. Thus it is semantically wrong. A grammar does not specify semantic rules, and they are not verified by a parser. You would need to ensure the validity of the provided name some other way, for example comparing it to a database of valid names.
Anatomy of a Grammar
To define the elements of a grammars, let us look at an example in the most used format to describe grammars: the Backus-Naur Form (BNF). This format has many variants, including the Extended Backus-Naur Form. The Extended variant has the advantage of including a simple way to denote repetitions. Another notable variant is the Augmented Backus-Naur Form which is mostly used to describe bidirectional communications protocols.
A typical rule in a Backus-Naur grammar looks like this:
::= __expression__
The is a nonterminal, which means that it can be replaced by the group of elements on the right, __expression__. The element __expression__ could contain other nonterminal symbols or terminal ones. Terminal symbols are simply the ones that do not appear as a anywhere in the grammar. A typical example of a terminal symbol is a string of characters, like “Hello”.
A rule can also be called production rule. Technically, it defines a transformation between the nonterminal, on the left, and the set of nonterminals and terminals, on the right.
Types of Grammars
There are mainly two kinds of grammars used in parsing: regular grammars and context-free grammars. Usually to a kind of grammar corresponds the same kind of language: a regular grammar defines a regular language and so on. However, there is also a more recent kind of grammars called Parsing Expression Grammar (PEG) that is equally powerful as context-free grammars and thus define a context-free language. The difference between the two is in the notation and the interpretation of the rules.
As we already mentioned, the two kinds of languages are in a hierarchy of complexity: regular languages are simpler than context-free languages.
A relatively easy way to distinguish the two grammars would be that the __expression__ of a regular grammar, that is to say the right side of the rule, could be only one of:
the empty stringa single terminal symbola single terminal symbol followed by a nonterminal symbol
In practice though this is harder to check, because a particular tool could allow using more terminal symbols in one definition. Then the tool itself will automatically transform this expression in an equivalent series of expressions all belonging to one of the three mentioned cases.
So you could write an expression that is incompatible with a regular language, but it will be transformed into the proper form. In other words, the tool could provide syntactic sugar for writing grammars.
In a later paragraph we are going to discuss more at length the different kinds of grammars and their formats.
Lexer
A lexer transforms a sequence of characters into a sequence of tokens
Lexers are also known as scanners or tokenizers. Lexers play a role in parsing, because they transform the initial input into a form that is more manageable by the proper parser, which works at a later stage. Typically lexers are easier to write than parsers. Although there are special cases when both are quite complicated, for instance in the case of C (see the lexer hack).
A very important part of the job of the lexer is dealing with whitespace. Most of the times you want the lexer to discard whitespace. That is because otherwise the parser would have to check for the presence of whitespace between every single token, which would quickly become annoying.
There are cases when you cannot do that, because whitespace is relevant to the language, like in the case of Python where it is used to identify a block of code. Even in these cases, though, usually it is the lexer that deals with the problem of distinguishing the relevant whitespace from the irrelevant one. This means that you want the lexer to understand which whitespace is relevant to parsing. For example, when parsing Python you want the lexer to check if whitespace defines indentation (relevant) or space between the keyword if and the following expression (irrelevant).
Where the Lexer Ends and the Parser Begins
Given that lexers are almost exclusively used in conjunction with parsers, the dividing line between the two can be blurred at times. That is because the parsing must produce a result that is useful for the particular need of a program. So there is not just one correct way of parsing something, but you care about only the one way that serves your needs.
For example, imagine that you are creating a program that must parse the logs of a server to save them in a database. For this goal, the lexer will identify the series of numbers and dots and transform them into an IPv4 token.
IPv4: [0-9]+ “. ” [0-9]+ “. ” [0-9]+
Then the parser will analyze the sequence of tokens to determine if it is a message, a warning, etc.
What would happen instead if you were developing software that had to use the IP address to identify the country of the visitor? Probably you would want the lexer to identity the octets of the address, for later use, and make IPv4 a parser element.
DOT: “. ”
OCTET: [0-9]+
ipv4: OCTET DOT OCTET DOT OCTET DOT OCTET
This is one example of how the same information can be parsed in different ways because of different goals.
Parser
In the context of parsing, “parser” can refer both to the software that performs the whole process and also just to the proper parser, that analyzes the tokens produced by the lexer. This is simply a consequence of the fact that the parser takes care of the most important and difficult part of the whole process of parsing. By most important we mean what the user cares about the most and will actually sees. In fact, as we said, the lexer works as a helper to facilitate the work of the parser.
In any sense you pick, the output of the parser is an organized structure of the code, usually a tree. The tree can be a parse tree or an abstract syntax tree. They are both trees, but they differ in how closely they represent the actual code written and the intermediate elements defined by the parser. The line between the two can be blurry at times; we are going to see their differences a bit better in a later paragraph.
The form of a tree is chosen because it is an easy and natural way to work with the code at different levels of detail. For example, a class in C# has one body, this body is made of one statement, the block statement, that is a list of statements enclosed in a pair of curly brackets, and so on…
Syntactic vs Semantic Correctness
A parser is a fundamental part of a compiler, or an interpreter, but of course can be part of a variety of other software. For example, in our article Generate diagrams from C# source code using Roslyn we parsed C# files to produce diagrams.
The parser can only check the syntactic correctness of a piece of code, but the compiler can use its output also in the process of checking the semantic validity of the same piece of code.
Let us see an example of code that is syntactically correct, but semantically incorrect.
int x = 10
int sum = x + y
The problem is that one variable (y) is never defined and thus, if executed, the program will fail. However the parser has no way of knowing this, because it does not keep track of variables, it just looks at the structure of the code.
A compiler instead would typically traverse the parse tree a first time and keeps a list of all the variables that are defined. Then it would traverse the parse tree a second time and checks whether the variables that are used are all properly defined. In this example they are not and it will throw an error. So that is one way the parse tree can also be used to check the semantics by the compiler.
Scannerless Parser
A scannerless parser, or more rarely a lexerless parser, is a parser that performs the tokenization (i. e., the trasformation of a sequence of characters into tokens) and the proper parsing in a single step. In theory having a separate lexer and parser is preferable because it allows a clearer separation of objectives and the creation of a more modular parser.
A scannerless parser is a better design for a language where a clear distinction between lexer and parser is difficult or unnecessary. An example is a parser for a markup language, where special markers are inserted in a sea of text. It can also facilitate the handling of languages where traditional lexing is difficult, like C. That is because a scannerless parser can more easily deal with complex tokenizations.
Issues With Parsing Real Programming Languages
In theory contemporary parsing is designed to handle real programming languages, but in practice there are challenges with some real programming languages. At least, it might be harder parsing them using normal parsing generator tools.
Context-sensitive Parts
Parsing tools are traditionally designed to handle context-free languages, but sometimes the languages are context-sensitive. This might be the case in order to simplify the life of programmers or simply because of a bad design. I remember reading about a programmer that thought he could produce a parser for C in a week, but then it found so many corner cases that a year later he was still working on it…
A typical example of context-sensitive elements are the so-called soft keywords, i. e., strings that can be considered keywords in certain places, but otherwise can be used as identifiers).
Whitespace
Whitespace plays a significant role in some languages. The most well-known example is Python, where the indentation of a statement indicates if it is part of a certain block of code.
In most places whitespace is irrelevant even in Python: spaces between words or keywords do not matter. The real problem is indentation, that is used to identify blocks of code. The simplest way to deal with it is to check the indentation at the start of the line and transform it into the proper token, i. e., to create a token when the indentation changes from the previous line.
In practice, a custom function in the lexer produces INDENT and DEDENT tokens, when the indentation increases or decreases. These tokens play the role that in C-like languages is played by curly brackets: they indicate the start and end of code blocks.
This approach makes the lexing context-sensitive, instead of context-free. This complicates parsing and you normally you would not want to do it, but you are forced to do in this case.
Multiple Syntaxes
Another common issue is dealing with the fact that a language might actually include a few different syntaxes. In other words, the same source file may contain sections of code that follow a different syntax. In the context of parsing effectively the same source file contains different languages. The most famous example is probably the C or C++ preprocessor, which is actually a fairly complicated language on its own and can magically appear inside any random C code.
An easier case to deal with are annotations, that are present in many contemporary programming languages. Among other things they can be used to process the code before it arrives to the compiler. They can command the annotation processor to transform the code in some way, for instance to execute a specific function before executing the annotated one. They are easier to manage, because they can appear only in specific places.
Dangling Else
The dangling else is a common problem in parsing linked to the if-then-else statement. Since the else clause is optional, a sequence of if statements could be ambiguous. For example this one.
if one
then if two
then two
else???
It is unclear if the else belongs to the first if or the second one.
To be fair, this is for the most part a problem of language design. Most of the solutions do not really complicate parsing that much, for example requiring the use of an endif or requiring the use of blocks to delimit the if statement when it includes an else clause.
However there are also languages that offer no solution, that is to say that are designed ambiguously, for example (you guessed it) C. The conventional approach is to associate the else to the nearest if statement, which makes the parsing context-sensitive.
Parsing Tree and Abstract Syntax Tree
There are two terms that are related and sometimes they are used interchangeably: Parse Tree and Abstract Syntax Tree (AST). Technically the parse tree could also be called Concrete Syntax Tree (CST), because it should reflect more concretely the actual syntax of the input, at least compared to the AST.
Conceptually they are very similar. They are both trees: there is a root that has nodes representing the whole source code. The root has children nodes, that contain subtrees representing smaller and smaller portions of code, until single tokens (terminals) appear in the tree.
The difference is in the level of abstraction: a parse tree might contain all the tokens which appeared in the program and possibly a set of intermediate rules. Instead the AST is a polished version of the parse tree, in which only the information relevant to understanding the code is maintained. We are going to see an example of an intermediate rule in the next section.
Some information might be absent both in the AST and the parse tree. For instance, comments and grouping symbols (i. e., parentheses) are usually not represented. Things like comments are superfluous for a program and grouping symbols are implicitly defined by the structure of the tree.
From Parse Tree to Abstract Syntax Tree
A parse tree is a representation of the code closer to the concrete syntax. It shows many details of the implementation of the parser. For instance, usually each rule corresponds to a specific type of a node. A parse tree is usually transformed into an AST by the user, possibly with some help from the parser generator. A common help allows to annotate some rule in the grammar, to exclude the corresponding nodes from the generated tree. Another one is an option to collapse some kinds of nodes if they have only one child.
This makes sense because the parse tree is easier to produce for the parser, since it is a direct representation of the parsing process. However the AST is simpler and easier to process by the following steps of the program. They typically include all the operations that you may want to perform on the tree: code validation, interpretation, compilation, etc…
Let us look at a simple example to show the difference between a parse tree and an AST. Let’s start with a look at an example grammar.
// lexer
PLUS: ‘+’
WORD_PLUS: ‘plus’
NUMBER: [0-9]+
// parser
// the pipe | symbol indicate an alternative between the two
sum: NUMBER (PLUS | WORD_PLUS) NUMBER
In this grammar we can define a sum using both the symbol plus (+) or the string plus as operators. Imagine that you have to parse the following code.
10 plus 21
These could be the resulting parse tree and abstract syntax tree.
A Parse Tree and Abstract Syntax Tree
In the AST the indication of the specific operator has disappeared and all that remains is the operation to be performed. The specific operator is an example of an intermediate rule.
Graphical Representation Of A Tree
The output of a parser is a tree, but the tree can also be represented in graphical ways. That is to allow an easier understanding for the developer. Some parsing generator tools can output a file in the DOT language, a language designed to describe graphs (a tree is a particular kind of graph). Then this file is fed to a program that can create a graphical representation starting from this textual description (e. g., Graphviz).
Let’s see a text, based on the previous sum example.
digraph sum {
sum -> 10;
sum -> 21;}
The appropriate tool can create the following graphical representation.
Example image of a tree created by graphviz
If you want to see a bit more of DOT you can read our article Language Server Protocol: A Language Server For DOT With Visual Studio Code. In that article we show show how to create a Visual Studio Code plugin that can handle DOT files.
Grammars
Grammars are a set of rules used to describe a language, so it comes naturally to study the formats of the rules. However there are also several elements of a typical grammar that could use further attention. Some of them are due to the fact that a grammar can also be used to define other duties or to execute some code.
Typical Grammar Issues
First we are going to talk about some special rules or issues you might encounter in parsing.
The Missing Tokens
If you read grammars you will probably encounter many in which only a few tokens are defined and not all of them. Like in this grammar:
greeting: “Hello” NAME
There is no definition for the token “Hello”, but since you know that a parser deals with tokens, you may ask yourself how is this possible. The answer is that some tools generate for you the corresponding token for a string literal, to save you some time.
Be aware that this might be possible only under certain conditions. For instance, with ANTLR you must define all tokens yourself if you define separate lexer and parser grammars.
Left-recursive Rules
In the context of parsers, an important feature is the support for left-recursive rules. This means that a rule starts with a reference to itself. Sometime this reference could also be indirect, that is to say it could appear in another rule referenced by the first one.
Consider for example arithmetic operations. An addition could be described as two expressions separated by the plus (+) symbol, but the operands of the additions could be other additions.
addition: expression ‘+’ expression
multiplication: expression ‘*’ expression
// an expression could be an addition or a multiplication or a number
expression: multiplication | addition | [0-9]+
In this example expression contains an indirect reference to itself via the rules addition and multiplication.
This description also matches multiple additions like 5 + 4 + 3. That is because it can be interpreted as expression (5) (‘+’) expression(4+3) (the rule addition: the first expression corresponds to the option [0-9]+, the second one is another addition). And then 4 + 3 itself can be divided into its two components: expression(4) (‘+’) expression(3) (the rule addition: both the first and second expression corresponds to the option [0-9]+).
The problem is that left-recursive rules may not be used with some parser generators. The alternative is a long chain of expressions, that takes care also of the precedence of operators. A typical grammar for a parser that does not support such rules would look similar to this one:
expression: addition
addition: multiplication (‘+’ multiplication)*
multiplication: atom (‘*’ atom)*
atom: [0-9]+
As you can see, the expressions are defined in the inverse order of precedence. So the parser would put the expression with the lower precedence at the lowest level of the three; thus they would be executed first.
Some parser generators support direct left-recursive rules, but not indirect ones. Notice that usually the issue is with the parsing algorithm itself, that does not support left-recursive rules. So the parser generator may transform rules written as left-recursive in the proper way to make them work with its algorithm. In this sense, left-recursive support may be (very useful) syntactic sugar.
How Left-recursive Rules Are Transformed
The specific way in which the rules are transformed vary from one parser generator to the other, however the logic remains the same. The expressions are divided in two groups: the ones with an operator and two operands and the atomic ones. In our example the only atomic expression is a number ([0-9]+), but it could also be an expression between parentheses ((5 + 4)). That is because in mathematics parentheses are used to increase the precedence of an expression.
Once you have these two groups: you maintain the order of the members of the second group and reverse the order of the members of the first group. The reason is that humans reason on a first come, first serve basis: it is easier to write the expressions in their order of precedence.
However the final form of the parsing is a tree, which operates on a different principle: you start working on the leaves and rise up. So that at the end of this process the root node contains the final result. Which means that in the parsing tree the atomic expressions are at the bottom, while the ones with operators appear in the inverse order to the one in which they are applied.
Predicates
Predicates, sometimes called syntactic or semantic predicates, are special rules that are matched only if a certain condition is met. The condition is defined with code in a programming language supported by the tool for which the grammar is written.
Their advantage is that they allow some form of context-sensitive parsing, which is sometime unavoidable to match certain elements. For instance, they can be used to determine if a sequence of characters that defines a soft keyword is used in a position where it would be a keyword (e. g., the previous token can be followed by the keyword) or it is a simple identifier.
The disadvantages are that they slow parsing down and they make the grammar dependent on said programming language. That is because the condition is expressed in a programming language and must be checked. And, of course, to check this condition you must execute the code in the corresponding programming language.
Embedded Actions
Embedded actions identify code that is executed every time the rule is matched. They have the clear disadvantage that they make the grammar harder to read, since the rules are surrounded by code. Furthermore, just like predicates, they break the separation between a grammar that describes the language and the code that manipulates the results of the parsing.
Actions are frequently used by less sophisticated parsing generators as the only way to easily execute some code when a node is matched. Using these parser generations, the only alternative would be to traverse the tree and execute the proper code yourself. More advanced tools allow to use the visitor pattern instead to execute arbitrary code when needed, and also to govern the traversing of the tree.
They can also be used to add certain tokens or change the generated tree. While ugly, this might be the only practical way to deal with complicated languages, like C, or specific issues, like whitespace in Python.
Formats
There are two main formats for a grammar: BNF (and its variants) and PEG. Many tools implement their own variants of these ideal formats. Some tools use custom formats altogether. A frequent custom format consists of a three parts grammar: options together with custom code, followed by the lexer section and finally the parser section.
Given that a BNF-like format is usually the foundation of a context-free grammar you could also see it identified like the CFG format.
Backus-Naur Form and Its Variants
Backus–Naur Form (BNF) is the most successful format and was even the basis of PEG. However it is quite simple, and thus it is not often used in its basic form. A more powerful variant is typically used.
To show why these variants were necessary let’s show an example in BNF: the description of a character.
::= “a” | “b” | “c” | “d” | “e” | “f” | “g” | “h” | “i” | “j” | “k” | “l” | “m” | “n” | “o” | “p” | “q” | “r” | “s” | “t” | “u” | “v” | “w” | “x” | “y” | “z”
::= “0” | “1” | “2” | “3” | “4” | “5” | “6” | “7” | “8” | “9”
::= |
The symbol can be transformed into any of the letters of the English alphabet, although in our example only lowercase letters are valid. A similar process happens for , which can indicate any among the alternative digits. The first issue is that you have to list all the alternatives individually; you cannot use character classes like you do with regular expressions. This is annoying, but usually manageable, unless of course you have to list all Unicode characters.
A much harder problem is that there is no easy way to denote optional elements or repetitions. So if you want to do that you have to rely on boolean logic and t
Compiler Design - Types of Parsing - Tutorialspoint

Compiler Design – Types of Parsing – Tutorialspoint

Syntax analyzers follow production rules defined by means of context-free grammar. The way the production rules are implemented (derivation) divides parsing into two types: top-down parsing and bottom-up parsing.
Top-down Parsing
When the parser starts constructing the parse tree from the start symbol and then tries to transform the start symbol to the input, it is called top-down parsing.
Recursive descent parsing: It is a common form of top-down parsing. It is called recursive as it uses recursive procedures to process the input. Recursive descent parsing suffers from backtracking.
Backtracking: It means, if one derivation of a production fails, the syntax analyzer restarts the process using different rules of same production. This technique may process the input string more than once to determine the right production.
Bottom-up Parsing
As the name suggests, bottom-up parsing starts with the input symbols and tries to construct the parse tree up to the start symbol.
Example:
Input string: a + b * c
Production rules:
S → E
E → E + T
E → E * T
E → T
T → id
Let us start bottom-up parsing
a + b * c
Read the input and check if any production matches with the input:
T + b * c
E + b * c
E + T * c
E * c
E * T
E
S

Frequently Asked Questions about parsing tutorial

How do you parse?

Traditionally, parsing is done by taking a sentence and breaking it down into different parts of speech. The words are placed into distinct grammatical categories, and then the grammatical relationships between the words are identified, allowing the reader to interpret the sentence.Jul 3, 2019

What is a parsing process?

Parsing, syntax analysis, or syntactic analysis is the process of analyzing a string of symbols, either in natural language, computer languages or data structures, conforming to the rules of a formal grammar. …

How does parse work?

Structure of a Parser The parser needs the lexer because it does not work directly on the text but on the output produced by the lexer. … A lexer and a parser work in sequence: the lexer scans the input and produces the matching tokens; the parser then scans the tokens and produces the parsing result.Dec 16, 2017

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