672 lines
23 KiB
TeX
672 lines
23 KiB
TeX
% Created 2020-03-03 Tue 17:18
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% Intended LaTeX compiler: pdflatex
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\documentclass[11pt]{article}
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\usepackage[utf8]{inputenc}
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\usepackage[T1]{fontenc}
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\usepackage{graphicx}
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\usepackage{grffile}
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\usepackage{longtable}
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\usepackage{wrapfig}
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\usepackage{rotating}
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\usepackage[normalem]{ulem}
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\usepackage{amsmath}
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\usepackage{textcomp}
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\usepackage{amssymb}
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\usepackage{capt-of}
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\usepackage{hyperref}
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\usepackage{algorithm}
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\usepackage{comment}
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\usepackage{algpseudocode}
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\usepackage{amsmath,amssymb,amsthm}
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\newtheorem{definition}{Definition}
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\usepackage{graphicx}
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\usepackage{listings}
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\usepackage{color}
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\author{Francesco Mecca}
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\date{}
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\title{Translation Verification of the pattern matching compiler}
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\hypersetup{
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pdfauthor={Francesco Mecca},
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pdftitle={Translation Verification of the pattern matching compiler},
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pdfkeywords={},
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pdfsubject={},
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pdfcreator={Emacs 26.3 (Org mode 9.1.9)},
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pdflang={English}}
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\begin{document}
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\maketitle
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\begin{comment}
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\section{{\bfseries\sffamily TODO} Scaletta [1/5]}
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\label{sec:org7578cff}
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\begin{itemize}
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\item[{$\boxtimes$}] Abstract
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\item[{$\boxminus$}] Background [40\%]
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\begin{itemize}
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\item[{$\boxtimes$}]
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\item[{$\square$}] Lambda code [0\%]
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\begin{itemize}
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\item[{$\square$}] Compiler optimizations
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\item[{$\square$}] other instructions
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\end{itemize}
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\item[{$\boxtimes$}] Pattern matching
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\item[{$\square$}] Symbolic execution
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\item[{$\square$}] Translation Validation
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\end{itemize}
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\item[{$\square$}] Translation validation of the Pattern Matching Compiler
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\begin{itemize}
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\item[{$\square$}] Source translation
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\begin{itemize}
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\item[{$\square$}] Formal Grammar
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\item[{$\square$}] Compilation of source patterns
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\item[{$\square$}] Rest?
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\end{itemize}
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\item[{$\square$}] Target translation
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\begin{itemize}
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\item[{$\square$}] Formal Grammar
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\item[{$\square$}] Symbolic execution of the lambda target
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\end{itemize}
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\item[{$\square$}] Equivalence between source and target
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\end{itemize}
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\item[{$\square$}] Proof of correctness
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\item[{$\square$}] Practical results
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\end{itemize}
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\end{comment}
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\begin{abstract}
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This dissertation presents an algorithm for the translation validation of the
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pattern matching compiler. Given the source representation of the target program and the
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target program compiled in untyped lambda form, the algoritmhm is capable of modelling
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the source program in terms of symbolic constraints on it's branches and apply symbolic
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execution on the untyped lambda representation in order to validate wheter the compilation
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produced a valid result.
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In this context a valid result means that for every input in the domain of the source
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program the untyped lambda translation produces the same output as the source program.
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The input of the program is modelled in terms of symbolic constraints closely related to
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the runtime representation of objects and the output consists of OCaml code
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blackboxes that are not evaluated in the context of the verification.
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\end{abstract}
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\section{Background}
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\label{sec:org5b6accf}
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\subsection{}
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\label{sec:org3c9e604}
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Objective Caml () is a dialect of the ML (Meta-Language) family of programming
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languages.
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shares many features with other dialects of ML, such as SML and Caml Light,
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The main features of ML languages are the use of the Hindley-Milner type system that
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provides many advantages with respect to static type systems of traditional imperative and object
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oriented language such as C, C++ and Java, such as:
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\begin{itemize}
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\item Polymorphism: in certain scenarios a function can accept more than one
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type for the input parameters. For example a function that computes the lenght of a
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list doesn't need to inspect the type of the elements of the list and for this reason
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a List.length function can accept lists of integers, lists of strings and in general
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lists of any type. Such languages offer polymorphic functions through subtyping at
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runtime only, while other languages such as C++ offer polymorphism through compile
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time templates and function overloading.
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With the Hindley-Milner type system each well typed function can have more than one
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type but always has a unique best type, called the \emph{principal type}.
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For example the principal type of the List.length function is "For any \emph{a}, function from
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list of \emph{a} to \emph{int}" and \emph{a} is called the \emph{type parameter}.
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\item Strong typing: Languages such as C and C++ allow the programmer to operate on data
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without considering its type, mainly through pointers. Other languages such as C\#
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and Go allow type erasure so at runtime the type of the data can't be queried.
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In the case of programming languages using an Hindley-Milner type system the
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programmer is not allowed to operate on data by ignoring or promoting its type.
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\item Type Inference: the principal type of a well formed term can be inferred without any
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annotation or declaration.
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\item Algebraic data types: types that are modelled by the use of two
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algebraic operations, sum and product.
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A sum type is a type that can hold of many different types of
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objects, but only one at a time. For example the sum type defined
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as \emph{A + B} can hold at any moment a value of type A or a value of
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type B. Sum types are also called tagged union or variants.
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A product type is a type constructed as a direct product
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of multiple types and contains at any moment one instance for
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every type of its operands. Product types are also called tuples
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or records. Algebraic data types can be recursive
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in their definition and can be combined.
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\end{itemize}
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Moreover ML languages are functional, meaning that functions are
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treated as first class citizens and variables are immutable,
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although mutable statements and imperative constructs are permitted.
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In addition to that features an object system, that provides
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inheritance, subtyping and dynamic binding, and modules, that
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provide a way to encapsulate definitions. Modules are checked
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statically and can be reificated through functors.
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\subsection{Lambda form compilation}
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\label{sec:org6065c14}
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\begin{comment}
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https://dev.realworld.org/compiler-backend.html
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\end{comment}
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provides compilation in form of a byecode executable with an
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optionally embeddable interpreter and a native executable that could
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be statically linked to provide a single file executable.
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After the typechecker has proven that the program is type safe,
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the compiler lower the code to \emph{Lambda}, an s-expression based
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language that assumes that its input has already been proved safe.
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On the \emph{Lambda} representation of the source program, the compiler
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performes a series of optimization passes before translating the
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lambda form to assembly code.
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\begin{enumerate}
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\item datatypes
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\label{sec:org7b158eb}
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Most native data types in , such as integers, tuples, lists,
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records, can be seen as instances of the following definition
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\begin{verbatim}
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type t = Nil | One of int | Cons of int * t
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\end{verbatim}
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that is a type \emph{t} with three constructors that define its complete
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signature.
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Every constructor has an arity. Nil, a constructor of arity 0, is
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called a constant constructor.
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\item Lambda form types
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\label{sec:org737fa2f}
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A lambda form target file produced by the compiler consists of a
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single s-expression. Every s-expression consist of \emph{(}, a sequence of
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elements separated by a whitespace and a closing \emph{)}.
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Elements of s-expressions are:
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\begin{itemize}
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\item Atoms: sequences of ascii letters, digits or symbols
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\item Variables
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\item Strings: enclosed in double quotes and possibly escaped
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\item S-expressions: allowing arbitrary nesting
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\end{itemize}
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There are several numeric types:
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\begin{itemize}
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\item integers: that us either 31 or 63 bit two's complement arithmetic
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depending on system word size, and also wrapping on overflow
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\item 32 bit and 64 bit integers: that use 32-bit and 64-bit two's complement arithmetic
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with wrap on overflow
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\item big integers: offer integers with arbitrary precision
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\item floats: that use IEEE754 double-precision (64-bit) arithmetic with
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the addition of the literals \emph{infinity}, \emph{neg\_infinity} and \emph{nan}.
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\end{itemize}
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The are varios numeric operations defined:
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\begin{itemize}
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\item Arithmetic operations: +, -, *, /, \% (modulo), neg (unary negation)
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\item Bitwise operations: \&, |, \^{}, <<, >> (zero-shifting), a>> (sign extending)
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\item Numeric comparisons: <, >, <=, >=, ==
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\end{itemize}
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\item Functions
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\label{sec:org369db83}
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Functions are defined using the following syntax, and close over all
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bindings in scope: (lambda (arg1 arg2 arg3) BODY)
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and are applied using the following syntax: (apply FUNC ARG ARG ARG)
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Evaluation is eager.
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\item Bindings
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\label{sec:org120bc74}
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The atom \emph{let} introduces a sequence of bindings:
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(let BINDING BINDING BINDING \ldots{} BODY)
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\item Other atoms
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\label{sec:org58bd28f}
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TODO: if, switch, stringswitch\ldots{}
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TODO: magari esempi
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\end{enumerate}
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\subsection{Pattern matching}
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\label{sec:org5d3b2f5}
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Pattern matching is a widely adopted mechanism to interact with ADT.
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C family languages provide branching on predicates through the use of
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if statements and switch statements.
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Pattern matching on the other hands express predicates through
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syntactic templates that also allow to bind on data structures of
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arbitrary shapes. One common example of pattern matching is the use of regular
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expressions on strings. provides pattern matching on ADT and
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primitive data types.
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The result of a pattern matching operation is always one of:
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\begin{itemize}
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\item this value does not match this pattern”
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\item this value matches this pattern, resulting the following bindings of
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names to values and the jump to the expression pointed at the
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pattern.
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\end{itemize}
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\begin{verbatim}
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type color = | Red | Blue | Green | Black | White
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match color with
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| Red -> print "red"
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| Blue -> print "red"
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| Green -> print "red"
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| _ -> print "white or black"
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\end{verbatim}
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provides tokens to express data destructoring.
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For example we can examine the content of a list with patten matching
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\begin{verbatim}
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begin match list with
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| [ ] -> print "empty list"
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| element1 :: [ ] -> print "one element"
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| (element1 :: element2) :: [ ] -> print "two elements"
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| head :: tail-> print "head followed by many elements"
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\end{verbatim}
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Parenthesized patterns, such as the third one in the previous example,
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matches the same value as the pattern without parenthesis.
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The same could be done with tuples
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\begin{verbatim}
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begin match tuple with
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| (Some _, Some _) -> print "Pair of optional types"
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| (Some _, None) | (None, Some _) -> print "Pair of optional types, one of which is null"
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| (None, None) -> print "Pair of optional types, both null"
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\end{verbatim}
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The pattern pattern₁ | pattern₂ represents the logical "or" of the
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two patterns pattern₁ and pattern₂. A value matches pattern₁ |
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pattern₂ if it matches pattern₁ or pattern₂.
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Pattern clauses can make the use of \emph{guards} to test predicates and
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variables can captured (binded in scope).
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\begin{verbatim}
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begin match token_list with
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| "switch"::var::"{"::rest -> ...
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| "case"::":"::var::rest when is_int var -> ...
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| "case"::":"::var::rest when is_string var -> ...
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| "}"::[ ] -> ...
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| "}"::rest -> error "syntax error: " rest
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\end{verbatim}
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Moreover, the pattern matching compiler emits a warning when a
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pattern is not exhaustive or some patterns are shadowed by precedent ones.
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\subsection{Symbolic execution}
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\label{sec:orge2e0205}
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\subsection{Translation validation}
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\label{sec:orgbafe7bc}
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Translators, such as translators and code generators, are huge pieces of
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software usually consisting of multiple subsystem and
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constructing an actual specification of a translator implementation for
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formal validation is a very long task. Moreover, different
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translators implement different algorithms, so the correctness proof of
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a translator cannot be generalized and reused to prove another translator.
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Translation validation is an alternative to the verification of
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existing translators that consists of taking the source and the target
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(compiled) program and proving \emph{a posteriori} their semantic equivalence.
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\begin{itemize}
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\item[{$\square$}] Techniques for translation validation
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\item[{$\square$}] What does semantically equivalent mean
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\item[{$\square$}] What happens when there is no semantic equivalence
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\item[{$\square$}] Translation validation through symbolic execution
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\end{itemize}
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\subsection{Translation validation of the Pattern Matching Compiler}
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\label{sec:org24ee133}
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\begin{enumerate}
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\item Source program
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\label{sec:org8c21912}
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The algorithm takes as its input a source program and translates it
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into an algebraic data structure called \emph{constraint\_tree}.
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\begin{verbatim}
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type constraint_tree =
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| Unreachable
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| Failure
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| Leaf of source_expr
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| Guard of source_blackbox * constraint_tree * constraint_tree
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| Node of accessor * (constructor * constraint_tree) list * constraint_tree
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\end{verbatim}
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Unreachable, Leaf of source\_expr and Failure are the terminals of the three.
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We distinguish
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\begin{itemize}
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\item Unreachable: statically it is known that no value can go there
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\item Failure: a value matching this part results in an error
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\item Leaf: a value matching this part results into the evaluation of a
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source blackbox of code
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\end{itemize}
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The algorithm doesn't support type-declaration-based analysis
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to know the list of constructors at a given type.
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Let's consider some trivial examples:
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\begin{verbatim}
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function true -> 1
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\end{verbatim}
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[ ] Converti a disegni
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Is translated to
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\begin{center}
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\begin{tabular}{l}
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Node ([(true, Leaf 1)], Failure)\\
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\end{tabular}
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\end{center}
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while
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\begin{verbatim}
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function
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true -> 1
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| false -> 2
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\end{verbatim}
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will give
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\begin{center}
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\begin{tabular}{l}
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Node ([(true, Leaf 1); (false, Leaf 2)])\\
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\end{tabular}
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\end{center}
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It is possible to produce Unreachable examples by using
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refutation clauses (a "dot" in the right-hand-side)
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\begin{verbatim}
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function
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true -> 1
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| false -> 2
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| _ -> .
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\end{verbatim}
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that gets translated into
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Node ([(true, Leaf 1); (false, Leaf 2)], Unreachable)
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We trust this annotation, which is reasonable as the type-checker
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verifies that it indeed holds.
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Guard nodes of the tree are emitted whenever a guard is found. Guards
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node contains a blackbox of code that is never evaluated and two
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branches, one that is taken in case the guard evaluates to true and
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the other one that contains the path taken when the guard evaluates to
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true.
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[ ] Finisci con Node
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[ ] Spiega del fallback
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[ ] rivedi compilazione per tenere in considerazione il tuo albero invece che le lambda
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[ ] Specifica che stesso algoritmo usato per compilare a lambda, piu` optimizations
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The source code of a pattern matching function in has the
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following form:
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\begin{center}
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\begin{tabular}{l}
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match variable with\\
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\(\vert{}\) pattern₁ -> expr₁\\
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\(\vert{}\) pattern₂ when guard -> expr₂\\
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\(\vert{}\) pattern₃ as var -> expr₃\\
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⋮\\
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\(\vert{}\) pₙ -> exprₙ\\
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\end{tabular}
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\end{center}
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and can include any expression that is legal for the compiler,
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such as "when" conditions and assignments. Patterns could or could not
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be exhaustive.
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Pattern matching code could also be written using the more compact form:
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\begin{center}
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\begin{tabular}{l}
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function\\
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\(\vert{}\) pattern₁ -> expr₁\\
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\(\vert{}\) pattern₂ when guard -> expr₂\\
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\(\vert{}\) pattern₃ as var -> expr₃\\
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⋮\\
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\(\vert{}\) pₙ -> exprₙ\\
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\end{tabular}
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\end{center}
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This BNF grammar describes formally the grammar of the source program:
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\begin{verbatim}
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start ::= "match" id "with" patterns | "function" patterns
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patterns ::= (pattern0|pattern1) pattern1+
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;; pattern0 and pattern1 are needed to distinguish the first case in which
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;; we can avoid writing the optional vertical line
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pattern0 ::= clause
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pattern1 ::= "|" clause
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clause ::= lexpr "->" rexpr
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lexpr ::= rule (ε|condition)
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rexpr ::= _code ;; arbitrary code
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rule ::= wildcard|variable|constructor_pattern|or_pattern ;;
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;; rules
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wildcard ::= "_"
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variable ::= identifier
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constructor_pattern ::= constructor (rule|ε) (assignment|ε)
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constructor ::= int|float|char|string|bool
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|unit|record|exn|objects|ref
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|list|tuple|array
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|variant|parameterized_variant ;; data types
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or_pattern ::= wildcard|variable|constructor_pattern ("|" wildcard|variable|constructor_pattern)+
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condition ::= "when" bexpr
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assignment ::= "as" id
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bexpr ::= _code ;; arbitrary code
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\end{verbatim}
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\begin{comment}
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Check that it is still this
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\end{comment}
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Patterns are of the form
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\begin{center}
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\begin{tabular}{ll}
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pattern & type of pattern\\
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\hline
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\_ & wildcard\\
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x & variable\\
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c(p₁,p₂,\ldots{},pₙ) & constructor pattern\\
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(p₁\(\vert{}\) p₂) & or-pattern\\
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\end{tabular}
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\end{center}
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During compilation by the translators expressions are compiled into
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lambda code and are referred as lambda code actions lᵢ.
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The entire pattern matching code is represented as a clause matrix
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that associates rows of patterns (p\(_{\text{i,1}}\), p\(_{\text{i,2}}\), \ldots{}, p\(_{\text{i,n}}\)) to
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lambda code action lⁱ
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\begin{equation*}
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(P → L) =
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\begin{pmatrix}
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p_{1,1} & p_{1,2} & \cdots & p_{1,n} & → l₁ \\
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p_{2,1} & p_{2,2} & \cdots & p_{2,n} & → l₂ \\
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\vdots & \vdots & \ddots & \vdots & → \vdots \\
|
||
p_{m,1} & p_{m,2} & \cdots & p_{m,n} & → lₘ
|
||
\end{pmatrix}
|
||
\end{equation*}
|
||
|
||
The pattern \emph{p} matches a value \emph{v}, written as p ≼ v, when one of the
|
||
following rules apply
|
||
|
||
\begin{center}
|
||
\begin{tabular}{llll}
|
||
\hline
|
||
\_ & ≼ & v & ∀v\\
|
||
x & ≼ & v & ∀v\\
|
||
(p₁ \(\vert{}\)$\backslash$ p₂) & ≼ & v & iff p₁ ≼ v or p₂ ≼ v\\
|
||
c(p₁, p₂, \ldots{}, pₐ) & ≼ & c(v₁, v₂, \ldots{}, vₐ) & iff (p₁, p₂, \ldots{}, pₐ) ≼ (v₁, v₂, \ldots{}, vₐ)\\
|
||
(p₁, p₂, \ldots{}, pₐ) & ≼ & (v₁, v₂, \ldots{}, vₐ) & iff pᵢ ≼ vᵢ ∀i ∈ [1..a]\\
|
||
\hline
|
||
\end{tabular}
|
||
\end{center}
|
||
|
||
When a value \emph{v} matches pattern \emph{p} we say that \emph{v} is an \emph{instance} of \emph{p}.
|
||
|
||
Considering the pattern matrix P we say that the value vector
|
||
\(\vec{v}\) = (v₁, v₂, \ldots{}, vᵢ) matches the line number i in P if and only if the following two
|
||
conditions are satisfied:
|
||
\begin{itemize}
|
||
\item p\(_{\text{i,1}}\), p\(_{\text{i,2}}\), \(\cdots{}\), p\(_{\text{i,n}}\) ≼ (v₁, v₂, \ldots{}, vᵢ)
|
||
\item ∀j < i p\(_{\text{j,1}}\), p\(_{\text{j,2}}\), \(\cdots{}\), p\(_{\text{j,n}}\) ⋠ (v₁, v₂, \ldots{}, vᵢ)
|
||
\end{itemize}
|
||
|
||
We can define the following three relations with respect to patterns:
|
||
\begin{itemize}
|
||
\item Patter p is less precise than pattern q, written p ≼ q, when all
|
||
instances of q are instances of p
|
||
\item Pattern p and q are equivalent, written p ≡ q, when their instances
|
||
are the same
|
||
\item Patterns p and q are compatible when they share a common instance
|
||
\end{itemize}
|
||
|
||
\begin{enumerate}
|
||
\item Initial state of the compilation
|
||
\label{sec:org9a7b624}
|
||
|
||
Given a source of the following form:
|
||
|
||
|
||
\begin{center}
|
||
\begin{tabular}{l}
|
||
match variable with\\
|
||
\(\vert{}\) pattern₁ -> e₁\\
|
||
\(\vert{}\) pattern₂ -> e₂\\
|
||
⋮\\
|
||
\(\vert{}\) pₘ -> eₘ\\
|
||
\end{tabular}
|
||
\end{center}
|
||
|
||
the initial input of the algorithm C consists of a vector of variables
|
||
\(\vec{x}\) = (x₁, x₂, \ldots{}, xₙ) of size \emph{n} where \emph{n} is the arity of
|
||
the type of \emph{x} and a clause matrix P → L of width n and height m.
|
||
That is:
|
||
|
||
\begin{equation*}
|
||
C((\vec{x} = (x₁, x₂, ..., xₙ),
|
||
\begin{pmatrix}
|
||
p_{1,1} & p_{1,2} & \cdots & p_{1,n} → l₁ \\
|
||
p_{2,1} & p_{2,2} & \cdots & p_{2,n} → l₂ \\
|
||
\vdots & \vdots & \ddots & \vdots → \vdots \\
|
||
p_{m,1} & p_{m,2} & \cdots & p_{m,n} → lₘ)
|
||
\end{pmatrix}
|
||
\end{equation*}
|
||
|
||
The base case C₀ of the algorithm is the case in which the \(\vec{x}\)
|
||
is empty, that is \(\vec{x}\) = (), then the result of the compilation
|
||
C₀ is l₁
|
||
\begin{equation*}
|
||
C₀((),
|
||
\begin{pmatrix}
|
||
→ l₁ \\
|
||
→ l₂ \\
|
||
→ \vdots \\
|
||
→ lₘ
|
||
\end{pmatrix})
|
||
) = l₁
|
||
\end{equation*}
|
||
|
||
When \(\vec{x}\) ≠ () then the compilation advances using one of the
|
||
following four rules:
|
||
|
||
\begin{enumerate}
|
||
\item Variable rule: if all patterns of the first column of P are wildcard patterns or
|
||
bind the value to a variable, then
|
||
|
||
\begin{equation*}
|
||
C(\vec{x}, P → L) = C((x₂, x₃, ..., xₙ), P' → L')
|
||
\end{equation*}
|
||
where
|
||
\begin{equation*}
|
||
\begin{pmatrix}
|
||
p_{1,2} & \cdots & p_{1,n} & → & (let & y₁ & x₁) & l₁ \\
|
||
p_{2,2} & \cdots & p_{2,n} & → & (let & y₂ & x₁) & l₂ \\
|
||
\vdots & \ddots & \vdots & → & \vdots & \vdots & \vdots & \vdots \\
|
||
p_{m,2} & \cdots & p_{m,n} & → & (let & yₘ & x₁) & lₘ
|
||
\end{pmatrix}
|
||
\end{equation*}
|
||
|
||
That means in every lambda action lᵢ there is a binding of x₁ to the
|
||
variable that appears on the pattern \$p\(_{\text{i,1}}\). Bindings are omitted
|
||
for wildcard patterns and the lambda action lᵢ remains unchanged.
|
||
|
||
\item Constructor rule: if all patterns in the first column of P are
|
||
constructors patterns of the form k(q₁, q₂, \ldots{}, qₙ) we define a
|
||
new matrix, the specialized clause matrix S, by applying the
|
||
following transformation on every row \emph{p}:
|
||
\begin{lstlisting}[mathescape,columns=fullflexible,basicstyle=\fontfamily{lmvtt}\selectfont,]
|
||
for every c ∈ Set of constructors
|
||
for i ← 1 .. m
|
||
let kᵢ ← constructor_of($p_{i,1}$)
|
||
if kᵢ = c then
|
||
p ← $q_{i,1}$, $q_{i,2}$, ..., $q_{i,n'}$, $p_{i,2}$, $p_{i,3}$, ..., $p_{i, n}$
|
||
\end{lstlisting}
|
||
Patterns of the form \(q_{i,j}\) matches on the values of the
|
||
constructor and we define new fresh variables y₁, y₂, \ldots{}, yₐ so
|
||
that the lambda action lᵢ becomes
|
||
\end{enumerate}
|
||
|
||
\begin{lstlisting}[mathescape,columns=fullflexible,basicstyle=\fontfamily{lmvtt}\selectfont,]
|
||
(let (y₁ (field 0 x₁))
|
||
(y₂ (field 1 x₁))
|
||
...
|
||
(yₐ (field (a-1) x₁))
|
||
lᵢ)
|
||
\end{lstlisting}
|
||
|
||
and the result of the compilation for the set of constructors
|
||
\{c₁, c₂, \ldots{}, cₖ\} is:
|
||
|
||
\begin{lstlisting}[mathescape,columns=fullflexible,basicstyle=\fontfamily{lmvtt}\selectfont,]
|
||
switch x₁ with
|
||
case c₁: l₁
|
||
case c₂: l₂
|
||
...
|
||
case cₖ: lₖ
|
||
default: exit
|
||
\end{lstlisting}
|
||
|
||
\begin{enumerate}
|
||
\item Orpat rule: there are various strategies for dealing with
|
||
or-patterns. The most naive one is to split the or-patterns.
|
||
For example a row p containing an or-pattern:
|
||
\begin{equation*}
|
||
(p_{i,1}|q_{i,1}|r_{i,1}), p_{i,2}, ..., p_{i,m} → lᵢ
|
||
\end{equation*}
|
||
results in three rows added to the clause matrix
|
||
\begin{equation*}
|
||
p_{i,1}, p_{i,2}, ..., p_{i,m} → lᵢ \\
|
||
\end{equation*}
|
||
\begin{equation*}
|
||
q_{i,1}, p_{i,2}, ..., p_{i,m} → lᵢ \\
|
||
\end{equation*}
|
||
\begin{equation*}
|
||
r_{i,1}, p_{i,2}, ..., p_{i,m} → lᵢ
|
||
\end{equation*}
|
||
\item Mixture rule:
|
||
When none of the previous rules apply the clause matrix P → L is
|
||
splitted into two clause matrices, the first P₁ → L₁ that is the
|
||
largest prefix matrix for which one of the three previous rules
|
||
apply, and P₂ → L₂ containing the remaining rows. The algorithm is
|
||
applied to both matrices.
|
||
\end{enumerate}
|
||
|
||
\begin{comment}
|
||
#+BEGIN_COMMENT
|
||
CITE paper?
|
||
#+END_COMMENT’
|
||
\end{comment}
|
||
\end{enumerate}
|
||
\end{enumerate}
|
||
\end{document}
|