Programming Languages: Application and Interpretation
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Functional Languages
Functional Programming Languages (FPL) 1. Definitions................................................................... 2 2. Applications ................................................................ 2 3. Examples..................................................................... 3 4. FPL Characteristics:.................................................... 3 5. Lambda calculus (LC)................................................. 4 6. Functions in FPLs ....................................................... 7 7. Modern functional languages...................................... 9 8. Scheme overview...................................................... 11 8.1. Get your own Scheme from MIT...................... 11 8.2. General overview.............................................. 11 8.3. Data Typing ...................................................... 12 8.4. Comments ......................................................... 12 8.5. Recursion Instead of Iteration........................... 13 8.6. Evaluation ......................................................... 14 8.7. Storing and using Scheme code ........................ 14 8.8. Variables ........................................................... 15 8.9. Data types.......................................................... 16 8.10. Arithmetic functions ......................................... 17 8.11. Selection functions............................................ 18 8.12. Iteration............................................................. 23 8.13. Defining functions ........................................... -
Thriving in a Crowded and Changing World: C++ 2006–2020
Thriving in a Crowded and Changing World: C++ 2006–2020 BJARNE STROUSTRUP, Morgan Stanley and Columbia University, USA Shepherd: Yannis Smaragdakis, University of Athens, Greece By 2006, C++ had been in widespread industrial use for 20 years. It contained parts that had survived unchanged since introduced into C in the early 1970s as well as features that were novel in the early 2000s. From 2006 to 2020, the C++ developer community grew from about 3 million to about 4.5 million. It was a period where new programming models emerged, hardware architectures evolved, new application domains gained massive importance, and quite a few well-financed and professionally marketed languages fought for dominance. How did C++ ś an older language without serious commercial backing ś manage to thrive in the face of all that? This paper focuses on the major changes to the ISO C++ standard for the 2011, 2014, 2017, and 2020 revisions. The standard library is about 3/4 of the C++20 standard, but this paper’s primary focus is on language features and the programming techniques they support. The paper contains long lists of features documenting the growth of C++. Significant technical points are discussed and illustrated with short code fragments. In addition, it presents some failed proposals and the discussions that led to their failure. It offers a perspective on the bewildering flow of facts and features across the years. The emphasis is on the ideas, people, and processes that shaped the language. Themes include efforts to preserve the essence of C++ through evolutionary changes, to simplify itsuse,to improve support for generic programming, to better support compile-time programming, to extend support for concurrency and parallel programming, and to maintain stable support for decades’ old code. -
Compiler Error Messages Considered Unhelpful: the Landscape of Text-Based Programming Error Message Research
Working Group Report ITiCSE-WGR ’19, July 15–17, 2019, Aberdeen, Scotland Uk Compiler Error Messages Considered Unhelpful: The Landscape of Text-Based Programming Error Message Research Brett A. Becker∗ Paul Denny∗ Raymond Pettit∗ University College Dublin University of Auckland University of Virginia Dublin, Ireland Auckland, New Zealand Charlottesville, Virginia, USA [email protected] [email protected] [email protected] Durell Bouchard Dennis J. Bouvier Brian Harrington Roanoke College Southern Illinois University Edwardsville University of Toronto Scarborough Roanoke, Virgina, USA Edwardsville, Illinois, USA Scarborough, Ontario, Canada [email protected] [email protected] [email protected] Amir Kamil Amey Karkare Chris McDonald University of Michigan Indian Institute of Technology Kanpur University of Western Australia Ann Arbor, Michigan, USA Kanpur, India Perth, Australia [email protected] [email protected] [email protected] Peter-Michael Osera Janice L. Pearce James Prather Grinnell College Berea College Abilene Christian University Grinnell, Iowa, USA Berea, Kentucky, USA Abilene, Texas, USA [email protected] [email protected] [email protected] ABSTRACT of evidence supporting each one (historical, anecdotal, and empiri- Diagnostic messages generated by compilers and interpreters such cal). This work can serve as a starting point for those who wish to as syntax error messages have been researched for over half of a conduct research on compiler error messages, runtime errors, and century. Unfortunately, these messages which include error, warn- warnings. We also make the bibtex file of our 300+ reference corpus ing, and run-time messages, present substantial difficulty and could publicly available. -
Panel: NSF-Sponsored Innovative Approaches to Undergraduate Computer Science
Panel: NSF-Sponsored Innovative Approaches to Undergraduate Computer Science Stephen Bloch (Adelphi University) Amruth Kumar (Ramapo College) Stanislav Kurkovsky (Central CT State University) Clif Kussmaul (Muhlenberg College) Matt Dickerson (Middlebury College), moderator Project Web site(s) Intervention Delivery Supervision Program http://programbydesign.org curriculum with supporting in class; software normally active, but can be by Design http://picturingprograms.org IDE, libraries, & texts and textbook are done other ways Stephen Bloch http://www.ccs.neu.edu/home/ free downloads matthias/HtDP2e/ or web-based NSF awards 0010064 http://racket-lang.org & 0618543 http://wescheme.org Problets http://www.problets.org in- or after-class problem- applet in none - teacher not needed, Amruth Kumar solving exercises on a browser although some adopters use programming concepts it in active mode too NSF award 0817187 Mobile Game http://www.mgdcs.com/ in-class or take-home PC passive - teacher as Development programming projects facilitator to answer Qs Stan Kurkovsky NSF award DUE-0941348 POGIL http://pogil.org in-class activity paper or web passive - teacher as Clif Kussmaul http://cspogil.org facilitator to answer Qs NSF award TUES 1044679 Project Course(s) Language(s) Focus Program Middle school, Usually Scheme-like teaching problem-solving process, by pre-AP CS in HS, languages leading into Java; particularly test-driven DesignStephen CS0, CS1, CS2 has also been done in Python, development and use of data Bloch in college ML, Java, Scala, ... types to guide coding & testing Problets AP-CS, CS I, CS 2. C, C++, Java, C# code tracing, debugging, Amruth Kumar also as refresher or expression evaluation, to switch languages predicting program state in other courses Mobile Game AP-CS, CS1, CS2 Java core OO programming; DevelopmentSt intro to advanced subjects an Kurkovsky such as AI, networks, security POGILClif CS1, CS2, SE, etc. -
Defining the Undefinedness of C
Technical Report: Defining the Undefinedness of C Chucky Ellison Grigore Ros, u University of Illinois {celliso2,grosu}@illinois.edu Abstract naturally capture undefined behavior simply by exclusion, be- This paper investigates undefined behavior in C and offers cause of the complexity of undefined behavior, it takes active a few simple techniques for operationally specifying such work to avoid giving many undefined programs semantics. behavior formally. A semantics-based undefinedness checker In addition, capturing the undefined behavior is at least as for C is developed using these techniques, as well as a test important as capturing the defined behavior, as it represents suite of undefined programs. The tool is evaluated against a source of many subtle program bugs. While a semantics other popular analysis tools, using the new test suite in of defined programs can be used to prove their behavioral addition to a third-party test suite. The semantics-based tool correctness, any results are contingent upon programs actu- performs at least as well or better than the other tools tested. ally being defined—it takes a semantics capturing undefined behavior to decide whether this is the case. 1. Introduction C, together with C++, is the king of undefined behavior—C has over 200 explicitly undefined categories of behavior, and A programming language specification or semantics has dual more that are left implicitly undefined [11]. Many of these duty: to describe the behavior of correct programs and to behaviors can not be detected statically, and as we show later identify incorrect programs. The process of identifying incor- (Section 2.6), detecting them is actually undecidable even rect programs can also be seen as describing which programs dynamically. -
The Next 700 Semantics: a Research Challenge Shriram Krishnamurthi Brown University [email protected] Benjamin S
The Next 700 Semantics: A Research Challenge Shriram Krishnamurthi Brown University [email protected] Benjamin S. Lerner Northeastern University [email protected] Liam Elberty Unaffiliated Abstract Modern systems consist of large numbers of languages, frameworks, libraries, APIs, and more. Each has characteristic behavior and data. Capturing these in semantics is valuable not only for understanding them but also essential for formal treatment (such as proofs). Unfortunately, most of these systems are defined primarily through implementations, which means the semantics needs to be learned. We describe the problem of learning a semantics, provide a structuring process that is of potential value, and also outline our failed attempts at achieving this so far. 2012 ACM Subject Classification Software and its engineering → General programming languages; Software and its engineering → Language features; Software and its engineering → Semantics; Software and its engineering → Formal language definitions Keywords and phrases Programming languages, desugaring, semantics, testing Digital Object Identifier 10.4230/LIPIcs.SNAPL.2019.9 Funding This work was partially supported by the US National Science Foundation and Brown University while all authors were at Brown University. Acknowledgements The authors thank Eugene Charniak and Kevin Knight for useful conversations. The reviewers provided useful feedback that improved the presentation. © Shriram Krishnamurthi and Benjamin S. Lerner and Liam Elberty; licensed under Creative Commons License CC-BY 3rd Summit on Advances in Programming Languages (SNAPL 2019). Editors: Benjamin S. Lerner, Rastislav Bodík, and Shriram Krishnamurthi; Article No. 9; pp. 9:1–9:14 Leibniz International Proceedings in Informatics Schloss Dagstuhl – Leibniz-Zentrum für Informatik, Dagstuhl Publishing, Germany 9:2 The Next 700 Semantics: A Research Challenge 1 Motivation Semantics is central to the trade of programming language researchers and practitioners. -
(Pdf) of from Push/Enter to Eval/Apply by Program Transformation
From Push/Enter to Eval/Apply by Program Transformation MaciejPir´og JeremyGibbons Department of Computer Science University of Oxford [email protected] [email protected] Push/enter and eval/apply are two calling conventions used in implementations of functional lan- guages. In this paper, we explore the following observation: when considering functions with mul- tiple arguments, the stack under the push/enter and eval/apply conventions behaves similarly to two particular implementations of the list datatype: the regular cons-list and a form of lists with lazy concatenation respectively. Along the lines of Danvy et al.’s functional correspondence between def- initional interpreters and abstract machines, we use this observation to transform an abstract machine that implements push/enter into an abstract machine that implements eval/apply. We show that our method is flexible enough to transform the push/enter Spineless Tagless G-machine (which is the semantic core of the GHC Haskell compiler) into its eval/apply variant. 1 Introduction There are two standard calling conventions used to efficiently compile curried multi-argument functions in higher-order languages: push/enter (PE) and eval/apply (EA). With the PE convention, the caller pushes the arguments on the stack, and jumps to the function body. It is the responsibility of the function to find its arguments, when they are needed, on the stack. With the EA convention, the caller first evaluates the function to a normal form, from which it can read the number and kinds of arguments the function expects, and then it calls the function body with the right arguments. -
Proceedings of the 8Th European Lisp Symposium Goldsmiths, University of London, April 20-21, 2015 Julian Padget (Ed.) Sponsors
Proceedings of the 8th European Lisp Symposium Goldsmiths, University of London, April 20-21, 2015 Julian Padget (ed.) Sponsors We gratefully acknowledge the support given to the 8th European Lisp Symposium by the following sponsors: WWWLISPWORKSCOM i Organization Programme Committee Julian Padget – University of Bath, UK (chair) Giuseppe Attardi — University of Pisa, Italy Sacha Chua — Toronto, Canada Stephen Eglen — University of Cambridge, UK Marc Feeley — University of Montreal, Canada Matthew Flatt — University of Utah, USA Rainer Joswig — Hamburg, Germany Nick Levine — RavenPack, Spain Henry Lieberman — MIT, USA Christian Queinnec — University Pierre et Marie Curie, Paris 6, France Robert Strandh — University of Bordeaux, France Edmund Weitz — University of Applied Sciences, Hamburg, Germany Local Organization Christophe Rhodes – Goldsmiths, University of London, UK (chair) Richard Lewis – Goldsmiths, University of London, UK Shivi Hotwani – Goldsmiths, University of London, UK Didier Verna – EPITA Research and Development Laboratory, France ii Contents Acknowledgments i Messages from the chairs v Invited contributions Quicklisp: On Beyond Beta 2 Zach Beane µKanren: Running the Little Things Backwards 3 Bodil Stokke Escaping the Heap 4 Ahmon Dancy Unwanted Memory Retention 5 Martin Cracauer Peer-reviewed papers Efficient Applicative Programming Environments for Computer Vision Applications 7 Benjamin Seppke and Leonie Dreschler-Fischer Keyboard? How quaint. Visual Dataflow Implemented in Lisp 15 Donald Fisk P2R: Implementation of -
Application and Interpretation
Programming Languages: Application and Interpretation Shriram Krishnamurthi Brown University Copyright c 2003, Shriram Krishnamurthi This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 United States License. If you create a derivative work, please include the version information below in your attribution. This book is available free-of-cost from the author’s Web site. This version was generated on 2007-04-26. ii Preface The book is the textbook for the programming languages course at Brown University, which is taken pri- marily by third and fourth year undergraduates and beginning graduate (both MS and PhD) students. It seems very accessible to smart second year students too, and indeed those are some of my most successful students. The book has been used at over a dozen other universities as a primary or secondary text. The book’s material is worth one undergraduate course worth of credit. This book is the fruit of a vision for teaching programming languages by integrating the “two cultures” that have evolved in its pedagogy. One culture is based on interpreters, while the other emphasizes a survey of languages. Each approach has significant advantages but also huge drawbacks. The interpreter method writes programs to learn concepts, and has its heart the fundamental belief that by teaching the computer to execute a concept we more thoroughly learn it ourselves. While this reasoning is internally consistent, it fails to recognize that understanding definitions does not imply we understand consequences of those definitions. For instance, the difference between strict and lazy evaluation, or between static and dynamic scope, is only a few lines of interpreter code, but the consequences of these choices is enormous. -
Programming Languages As Operating Systems (Or Revenge of the Son of the Lisp Machine)
Programming Languages as Operating Systems (or Revenge of the Son of the Lisp Machine) Matthew Flatt Robert Bruce Findler Shriram Krishnamurthi Matthias Felleisen Department of Computer Science∗ Rice University Houston, Texas 77005-1892 Abstract reclaim the program’s resources—even though the program and DrScheme share a single virtual machine. The MrEd virtual machine serves both as the implementa- To address this problem, MrEd provides a small set of tion platform for the DrScheme programming environment, new language constructs. These constructs allow a program- and as the underlying Scheme engine for executing expres- running program, such as DrScheme, to run nested programs sions and programs entered into DrScheme’s read-eval-print directly on the MrEd virtual machine without sacrificing loop. We describe the key elements of the MrEd virtual control over the nested programs. As a result, DrScheme machine for building a programming environment, and we can execute a copy of DrScheme that is executing its own step through the implementation of a miniature version of copy of DrScheme (see Figure 1). The inner and middle DrScheme in MrEd. More generally, we show how MrEd de- DrSchemes cannot interfere with the operation of the outer fines a high-level operating system for graphical programs. DrScheme, and the middle DrScheme cannot interfere with the outer DrScheme’s control over the inner DrScheme. 1 MrEd: A Scheme Machine In this paper, we describe the key elements of the MrEd virtual machine, and we step through the implementation The DrScheme programming environment [10] provides stu- of a miniature version of DrScheme in MrEd. -
Automatic Detection of Unspecified Expression Evaluation in Freertos Programs
IT 14 022 Examensarbete 30 hp Juni 2014 Automatic Detection of Unspecified Expression Evaluation in FreeRTOS Programs Shahrzad Khodayari Institutionen för informationsteknologi Department of Information Technology . ... ... .... . . .. . . Acknowledgements This is a master thesis submitted in Embedded Systems to Department of Information Technology, Uppsala University, Uppsala, Sweden. I would like to express my deepest gratitude to my suppervisor Philipp Rümmer, Programme Director for Master’s programme in Embedded System at Uppsala University, for his patience in supporting continuously and generously guiding me with this project. I would like to appriciate professor Bengt Jonsson for reviewing my master thesis and offering valuable suggestions and comments. I would like to thank professor Daniel Kroening for helping me and providing updates of CBMC. Sincere thanks to my husband and my incredible parents who gave me courage and support throughout the project. Contents 1 Introduction..........................................................................................................1 Contributions.................................................................................................................3 Structure of the thesis report..........................................................................................3 2 Background...........................................................................................................5 2.1 Verification..............................................................................................................5 -
Making a Faster Curry with Extensional Types
Making a Faster Curry with Extensional Types Paul Downen Simon Peyton Jones Zachary Sullivan Microsoft Research Zena M. Ariola Cambridge, UK University of Oregon [email protected] Eugene, Oregon, USA [email protected] [email protected] [email protected] Abstract 1 Introduction Curried functions apparently take one argument at a time, Consider these two function definitions: which is slow. So optimizing compilers for higher-order lan- guages invariably have some mechanism for working around f1 = λx: let z = h x x in λy:e y z currying by passing several arguments at once, as many as f = λx:λy: let z = h x x in e y z the function can handle, which is known as its arity. But 2 such mechanisms are often ad-hoc, and do not work at all in higher-order functions. We show how extensional, call- It is highly desirable for an optimizing compiler to η ex- by-name functions have the correct behavior for directly pand f1 into f2. The function f1 takes only a single argu- expressing the arity of curried functions. And these exten- ment before returning a heap-allocated function closure; sional functions can stand side-by-side with functions native then that closure must subsequently be called by passing the to practical programming languages, which do not use call- second argument. In contrast, f2 can take both arguments by-name evaluation. Integrating call-by-name with other at once, without constructing an intermediate closure, and evaluation strategies in the same intermediate language ex- this can make a huge difference to run-time performance in presses the arity of a function in its type and gives a princi- practice [Marlow and Peyton Jones 2004].