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Source-To-Source Translation and Software Engineering
Journal of Software Engineering and Applications, 2013, 6, 30-40 http://dx.doi.org/10.4236/jsea.2013.64A005 Published Online April 2013 (http://www.scirp.org/journal/jsea) Source-to-Source Translation and Software Engineering David A. Plaisted Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, USA. Email: [email protected] Received February 5th, 2013; revised March 7th, 2013; accepted March 15th, 2013 Copyright © 2013 David A. Plaisted. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. ABSTRACT Source-to-source translation of programs from one high level language to another has been shown to be an effective aid to programming in many cases. By the use of this approach, it is sometimes possible to produce software more cheaply and reliably. However, the full potential of this technique has not yet been realized. It is proposed to make source- to-source translation more effective by the use of abstract languages, which are imperative languages with a simple syntax and semantics that facilitate their translation into many different languages. By the use of such abstract lan- guages and by translating only often-used fragments of programs rather than whole programs, the need to avoid writing the same program or algorithm over and over again in different languages can be reduced. It is further proposed that programmers be encouraged to write often-used algorithms and program fragments in such abstract languages. Libraries of such abstract programs and program fragments can then be constructed, and programmers can be encouraged to make use of such libraries by translating their abstract programs into application languages and adding code to join things together when coding in various application languages. -
A Proof of Cantor's Theorem
Cantor’s Theorem Joe Roussos 1 Preliminary ideas Two sets have the same number of elements (are equinumerous, or have the same cardinality) iff there is a bijection between the two sets. Mappings: A mapping, or function, is a rule that associates elements of one set with elements of another set. We write this f : X ! Y , f is called the function/mapping, the set X is called the domain, and Y is called the codomain. We specify what the rule is by writing f(x) = y or f : x 7! y. e.g. X = f1; 2; 3g;Y = f2; 4; 6g, the map f(x) = 2x associates each element x 2 X with the element in Y that is double it. A bijection is a mapping that is injective and surjective.1 • Injective (one-to-one): A function is injective if it takes each element of the do- main onto at most one element of the codomain. It never maps more than one element in the domain onto the same element in the codomain. Formally, if f is a function between set X and set Y , then f is injective iff 8a; b 2 X; f(a) = f(b) ! a = b • Surjective (onto): A function is surjective if it maps something onto every element of the codomain. It can map more than one thing onto the same element in the codomain, but it needs to hit everything in the codomain. Formally, if f is a function between set X and set Y , then f is surjective iff 8y 2 Y; 9x 2 X; f(x) = y Figure 1: Injective map. -
An Update on the Four-Color Theorem Robin Thomas
thomas.qxp 6/11/98 4:10 PM Page 848 An Update on the Four-Color Theorem Robin Thomas very planar map of connected countries the five-color theorem (Theorem 2 below) and can be colored using four colors in such discovered what became known as Kempe chains, a way that countries with a common and Tait found an equivalent formulation of the boundary segment (not just a point) re- Four-Color Theorem in terms of edge 3-coloring, ceive different colors. It is amazing that stated here as Theorem 3. Esuch a simply stated result resisted proof for one The next major contribution came in 1913 from and a quarter centuries, and even today it is not G. D. Birkhoff, whose work allowed Franklin to yet fully understood. In this article I concentrate prove in 1922 that the four-color conjecture is on recent developments: equivalent formulations, true for maps with at most twenty-five regions. The a new proof, and progress on some generalizations. same method was used by other mathematicians to make progress on the four-color problem. Im- Brief History portant here is the work by Heesch, who developed The Four-Color Problem dates back to 1852 when the two main ingredients needed for the ultimate Francis Guthrie, while trying to color the map of proof—“reducibility” and “discharging”. While the the counties of England, noticed that four colors concept of reducibility was studied by other re- sufficed. He asked his brother Frederick if it was searchers as well, the idea of discharging, crucial true that any map can be colored using four col- for the unavoidability part of the proof, is due to ors in such a way that adjacent regions (i.e., those Heesch, and he also conjectured that a suitable de- sharing a common boundary segment, not just a velopment of this method would solve the Four- point) receive different colors. -
Bringing GNU Emacs to Native Code
Bringing GNU Emacs to Native Code Andrea Corallo Luca Nassi Nicola Manca [email protected] [email protected] [email protected] CNR-SPIN Genoa, Italy ABSTRACT such a long-standing project. Although this makes it didactic, some Emacs Lisp (Elisp) is the Lisp dialect used by the Emacs text editor limitations prevent the current implementation of Emacs Lisp to family. GNU Emacs can currently execute Elisp code either inter- be appealing for broader use. In this context, performance issues preted or byte-interpreted after it has been compiled to byte-code. represent the main bottleneck, which can be broken down in three In this work we discuss the implementation of an optimizing com- main sub-problems: piler approach for Elisp targeting native code. The native compiler • lack of true multi-threading support, employs the byte-compiler’s internal representation as input and • garbage collection speed, exploits libgccjit to achieve code generation using the GNU Com- • code execution speed. piler Collection (GCC) infrastructure. Generated executables are From now on we will focus on the last of these issues, which con- stored as binary files and can be loaded and unloaded dynamically. stitutes the topic of this work. Most of the functionality of the compiler is written in Elisp itself, The current implementation traditionally approaches the prob- including several optimization passes, paired with a C back-end lem of code execution speed in two ways: to interface with the GNU Emacs core and libgccjit. Though still a work in progress, our implementation is able to bootstrap a func- • Implementing a large number of performance-sensitive prim- tional Emacs and compile all lexically scoped Elisp files, including itive functions (also known as subr) in C. -
Fibonacci, Kronecker and Hilbert NKS 2007
Fibonacci, Kronecker and Hilbert NKS 2007 Klaus Sutner Carnegie Mellon University www.cs.cmu.edu/∼sutner NKS’07 1 Overview • Fibonacci, Kronecker and Hilbert ??? • Logic and Decidability • Additive Cellular Automata • A Knuth Question • Some Questions NKS’07 2 Hilbert NKS’07 3 Entscheidungsproblem The Entscheidungsproblem is solved when one knows a procedure by which one can decide in a finite number of operations whether a given logical expression is generally valid or is satisfiable. The solution of the Entscheidungsproblem is of fundamental importance for the theory of all fields, the theorems of which are at all capable of logical development from finitely many axioms. D. Hilbert, W. Ackermann Grundzuge¨ der theoretischen Logik, 1928 NKS’07 4 Model Checking The Entscheidungsproblem for the 21. Century. Shift to computer science, even commercial applications. Fix some suitable logic L and collection of structures A. Find efficient algorithms to determine A |= ϕ for any structure A ∈ A and sentence ϕ in L. Variants: fix ϕ, fix A. NKS’07 5 CA as Structures Discrete dynamical systems, minimalist description: Aρ = hC, i where C ⊆ ΣZ is the space of configurations of the system and is the “next configuration” relation induced by the local map ρ. Use standard first order logic (either relational or functional) to describe properties of the system. NKS’07 6 Some Formulae ∀ x ∃ y (y x) ∀ x, y, z (x z ∧ y z ⇒ x = y) ∀ x ∃ y, z (y x ∧ z x ∧ ∀ u (u x ⇒ u = y ∨ u = z)) There is no computability requirement for configurations, in x y both x and y may be complicated. -
Extending Fortran with Meta-Programming
OpenFortran: Extending Fortran with Meta-programming Songqing Yue and Jeff Gray Department of Computer Science University of Alabama Tuscaloosa, AL, USA {syue, gray}@cs.ua.edu Abstract—Meta-programming has shown much promise for manipulated [1]. MOPs add the ability of meta-programming improving the quality of software by offering programming to programming languages by providing users with standard language techniques to address issues of modularity, reusability, interfaces to modify the internal implementation of the maintainability, and extensibility. A system that supports meta- program. Through the interface, programmers can programming is able to generate or manipulate other programs incrementally change the implementation and the behavior of a to extend their behavior. This paper describes OpenFortran, a program to better suit their needs. Furthermore, a metaprogram Meta-Object Protocol (MOP) that is able to bring the power of can capture the needs of a commonly needed feature and be meta-programming to Fortran, one of the most widely used applied to several different base programs. languages in the area of High Performance Computing (HPC). OpenFortran provides a framework that allows library A MOP may perform the underling adaptation of the base developers to build arbitrary source-to-source program program at either run-time or compile-time. Run-time MOPs transformation libraries for Fortran programs. To relieve the function while a program is executing and can be used to burden of learning the details about how the underlying perform real-time adaptation, for example the Common Lisp transformations are performed, a domain-specific language Object System (CLOS) [13] that allows the mechanisms of named SPOT has been created that allows developers to specify inheritance, method dispatching, and class instantiation to be direct manipulation of programs. -
ROSE Tutorial: a Tool for Building Source-To-Source Translators Draft Tutorial (Version 0.9.11.115)
ROSE Tutorial: A Tool for Building Source-to-Source Translators Draft Tutorial (version 0.9.11.115) Daniel Quinlan, Markus Schordan, Richard Vuduc, Qing Yi Thomas Panas, Chunhua Liao, and Jeremiah J. Willcock Lawrence Livermore National Laboratory Livermore, CA 94550 925-423-2668 (office) 925-422-6278 (fax) fdquinlan,panas2,[email protected] [email protected] [email protected] [email protected] [email protected] Project Web Page: www.rosecompiler.org UCRL Number for ROSE User Manual: UCRL-SM-210137-DRAFT UCRL Number for ROSE Tutorial: UCRL-SM-210032-DRAFT UCRL Number for ROSE Source Code: UCRL-CODE-155962 ROSE User Manual (pdf) ROSE Tutorial (pdf) ROSE HTML Reference (html only) September 12, 2019 ii September 12, 2019 Contents 1 Introduction 1 1.1 What is ROSE.....................................1 1.2 Why you should be interested in ROSE.......................2 1.3 Problems that ROSE can address...........................2 1.4 Examples in this ROSE Tutorial...........................3 1.5 ROSE Documentation and Where To Find It.................... 10 1.6 Using the Tutorial................................... 11 1.7 Required Makefile for Tutorial Examples....................... 11 I Working with the ROSE AST 13 2 Identity Translator 15 3 Simple AST Graph Generator 19 4 AST Whole Graph Generator 23 5 Advanced AST Graph Generation 29 6 AST PDF Generator 31 7 Introduction to AST Traversals 35 7.1 Input For Example Traversals............................. 35 7.2 Traversals of the AST Structure............................ 36 7.2.1 Classic Object-Oriented Visitor Pattern for the AST............ 37 7.2.2 Simple Traversal (no attributes)....................... 37 7.2.3 Simple Pre- and Postorder Traversal.................... -
Towards Large-Scale Refactoring for Ocaml
Towards Large-scale Refactoring for OCaml REUBEN N. S. ROWE, University of Kent, UK SIMON J. THOMPSON, University of Kent, UK Refactoring is the process of changing the way a program works without changing its overall behaviour. The functional programming paradigm presents its own unique challenges to refactoring. For the OCaml language in particular, the expressiveness of its module system makes this a highly non-trivial task. The use of PPX preprocessors, other language extensions, and idiosyncratic build systems complicates matters further. We begin to address the question of how to refactor large OCaml programs by looking at a particular refactoring—value binding renaming—and implementing a prototype tool to carry it out. Our tool, Rotor, is developed in OCaml itself and combines several features to manage the complexities of refactoring OCaml code. Firstly it defines a rich, hierarchical way of identifying bindings which distinguishes between structures and functors and their associated module types, and is able to refer directly to functor parameters. Secondly it makes use of the recently developed visitors library to perform generic traversals of abstract syntax trees. Lastly it implements a notion of ‘dependency’ between renamings, allowing refactorings to be computed in a modular fashion. We evaluate Rotor using a snapshot of Jane Street’s core library and its dependencies, comprising some 900 source files across 80 libraries, and a test suite of around 3000 renamings. We propose that the notion of dependency is a general one for refactoring, distinct from a refactoring ‘precondition’. Dependencies may actually be mutual, in that all must be applied together for each one individually to be correct, and serve as declarative specifications of refactorings. -
Combining Source and Target Level Cost Analyses for Ocaml Programs
Combining Source and Target Level Cost Analyses for OCaml Programs Stefan K. Muller Jan Hoffmann Carnegie Mellon University Carnegie Mellon University Abstract bounding gas usage in smart contracts [24]. More generally, Deriving resource bounds for programs is a well-studied it is an appealing idea to provide programmers with imme- problem in the programming languages community. For com- diate feedback about the efficiency of their code at design piled languages, there is a tradeoff in deciding when during time. compilation to perform such a resource analysis. Analyses at When designing a resource analysis for a compiled higher- the level of machine code can precisely bound the wall-clock level language, there is a tension between working on the execution time of programs, but often require manual anno- source code, the target code, or an intermediate represen- tations describing loop bounds and memory access patterns. tation. Advantages of analyzing the source code include Analyses that run on source code can more effectively derive more effective interaction with the programmer and more such information from types and other source-level features, opportunities for automation since the control flow and type but produce abstract bounds that do not directly reflect the information are readily available. The advantage of analyz- execution time of the compiled machine code. ing the target code is that analysis results apply to the code This paper proposes and compares different techniques for that is eventually executed. Many of the tools developed combining source-level and target-level resource analyses in in the programming languages community operate on the the context of the functional programming language OCaml. -
Hop Client-Side Compilation
Chapter 1 Hop Client-Side Compilation Florian Loitsch1, Manuel Serrano1 Abstract: Hop is a new language for programming interactive Web applications. It aims to replace HTML, JavaScript, and server-side scripting languages (such as PHP, JSP) with a unique language that is used for client-side interactions and server-side computations. A Hop execution platform is made of two compilers: one that compiles the code executed by the server, and one that compiles the code executed by the client. This paper presents the latter. In order to ensure compatibility of Hop graphical user interfaces with popular plain Web browsers, the client-side Hop compiler has to generate regular HTML and JavaScript code. The generated code runs roughly at the same speed as hand- written code. Since the Hop language is built on top of the Scheme program- ming language, compiling Hop to JavaScript is nearly equivalent to compiling Scheme to JavaScript. SCM2JS, the compiler we have designed, supports the whole Scheme core language. In particular, it features proper tail recursion. How- ever complete proper tail recursion may slow down the generated code. Despite an optimization which eliminates up to 40% of instrumentation for tail call in- tensive benchmarks, worst case programs were more than two times slower. As a result Hop only uses a weaker form of tail-call optimization which simplifies recursive tail-calls to while-loops. The techniques presented in this paper can be applied to most strict functional languages such as ML and Lisp. SCM2JS can be downloaded at http://www-sop.inria.fr/mimosa/person- nel/Florian.Loitsch/scheme2js/. -
SI 413, Unit 3: Advanced Scheme
SI 413, Unit 3: Advanced Scheme Daniel S. Roche ([email protected]) Fall 2018 1 Pure Functional Programming Readings for this section: PLP, Sections 10.7 and 10.8 Remember there are two important characteristics of a “pure” functional programming language: • Referential Transparency. This fancy term just means that, for any expression in our program, the result of evaluating it will always be the same. In fact, any referentially transparent expression could be replaced with its value (that is, the result of evaluating it) without changing the program whatsoever. Notice that imperative programming is about as far away from this as possible. For example, consider the C++ for loop: for ( int i = 0; i < 10;++i) { /∗ some s t u f f ∗/ } What can we say about the “stuff” in the body of the loop? Well, it had better not be referentially transparent. If it is, then there’s no point in running over it 10 times! • Functions are First Class. Another way of saying this is that functions are data, just like any number or list. Functions are values, in fact! The specific privileges that a function earns by virtue of being first class include: 1) Functions can be given names. This is not a big deal; we can name functions in pretty much every programming language. In Scheme this just means we can do (define (f x) (∗ x 3 ) ) 2) Functions can be arguments to other functions. This is what you started to get into at the end of Lab 2. For starters, there’s the basic predicate procedure?: (procedure? +) ; #t (procedure? 10) ; #f (procedure? procedure?) ; #t 1 And then there are “higher-order functions” like map and apply: (apply max (list 5 3 10 4)) ; 10 (map sqrt (list 16 9 64)) ; '(4 3 8) What makes the functions “higher-order” is that one of their arguments is itself another function. -
The Axiom of Choice and Its Implications
THE AXIOM OF CHOICE AND ITS IMPLICATIONS KEVIN BARNUM Abstract. In this paper we will look at the Axiom of Choice and some of the various implications it has. These implications include a number of equivalent statements, and also some less accepted ideas. The proofs discussed will give us an idea of why the Axiom of Choice is so powerful, but also so controversial. Contents 1. Introduction 1 2. The Axiom of Choice and Its Equivalents 1 2.1. The Axiom of Choice and its Well-known Equivalents 1 2.2. Some Other Less Well-known Equivalents of the Axiom of Choice 3 3. Applications of the Axiom of Choice 5 3.1. Equivalence Between The Axiom of Choice and the Claim that Every Vector Space has a Basis 5 3.2. Some More Applications of the Axiom of Choice 6 4. Controversial Results 10 Acknowledgments 11 References 11 1. Introduction The Axiom of Choice states that for any family of nonempty disjoint sets, there exists a set that consists of exactly one element from each element of the family. It seems strange at first that such an innocuous sounding idea can be so powerful and controversial, but it certainly is both. To understand why, we will start by looking at some statements that are equivalent to the axiom of choice. Many of these equivalences are very useful, and we devote much time to one, namely, that every vector space has a basis. We go on from there to see a few more applications of the Axiom of Choice and its equivalents, and finish by looking at some of the reasons why the Axiom of Choice is so controversial.