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ACM Transactions on Programming Languages and Systems 1997: Volume 19 , Issue Index. Digitized from IA1534625-06 . Previous issue: sim_acm-transactions-on-programming-languages-and-systems_1996-11_18_6 . Next issue: sim_acm-transactions-on-programming-languages-and-systems_1997-01_19_1 .

In my talk I will report on the work in progress. The initial idea of the project was to test available compilers of C-like languages. Our plan is the following: Choose a reasonable subset of the language to be tested. Formalize the semantics of the sub-language in AsmL. Produce tons of different programs. Using the AsmL Test tool, build test sequences for all these programs to check the correctness of the executable binary produced by the compiler. We started with a very simple fragment of the language (if, while, break, goto, simple function call). The next reasonable step is to add exceptions handling, arithmetical expressions, classes. (Joint work with Andrey Novikov.) ©2004 Microsoft Corporation. All rights reserved.

Roel Van de Paar

Roel Van de Paar

Procedural Reflection in Programming Languages Brian Cantwell Smith MIT 1982

COMPARATIVE PROGRAMMING LANGUAGES Course Description: While most of the industry uses either procedural or object-oriented programming languages, there are entire families of other languages with certain strengths and weaknesses that make them attractive to a variety of problem domains. This course will introduce and discuss both the history and relevant features of both imperative and non-imperative programming languages. The course will cover topics such as syntax and semantics, essential concepts including data types, control structures, subprograms and advanced topics such as polymorphism, and object-oriented programming.  The course will explore both functional and logic programming as examples of non-imperative programming languages.  This course is a bit different from other courses within the curriculum in that it has no project assignments. Several of the units have non-graded exercises that provide hands-on exposure to some of the programming language concepts that will be studied within the course, however, the course is designed to be primarily a theory course. Required Textbook and Materials: Ben-Ari, M. (2006). Understanding programming languages. Weizman Institute of Science. This text is used with the permission of the author M. Ben-Ari. Download textbook here Learning Objectives and Outcomes:     Recognize the various schools or paradigms of programming languages including imperative languages (procedural and scripting languages, data-oriented languages, object-oriented languages) and non-imperative languages (functional and logic programming).     Define the principles and components of programming language design, such as control structures, names, and data types.     Recognize elements of syntax and semantics for a language.     Recognize strong vs. weak and static vs. dynamic typing in programming languages.     State the basic requirements for effective programming language design.     Identify the limitations of programming languages from within a historical perspective.     Classify the programming constructs that appear in imperative, functional, and logic programming languages. Course Topics: Unit 1– Introduction to Programming Languages Unit 2- Elements of Programming Languages Unit 3– Data Types Unit 4– Control Structures and Subprograms Unit 5– Object-Oriented Programming Unit 6- Scripting Languages Unit 7- Functional Programming Unit 8- Logic Programming Software Requirements/Installation: Unit 1: Turing Machine Simulator: http://morphett.info/turing/turing.html Little Man Computer Simulator: http://www.yorku.ca/sychen/research/LMC/index.html (simulation of von Neumann architecture) Unit 5: Online Interpreter for Peal Scripts: http://codepad.org/ Online Interpreter for Python Scripts: http://codepad.org/ Online Interpreter for JavaScript: http://writecodeonline.com/javascript/ Online Regular expression tester: http://myregexp.com/signedJar.html Unit 6: Online interpreter for Java: http://ideone.com/ Unit 7: Online interpreter for Haskell: http://ideone.com/ Unit 8: Online interpreter for Prolog: http://ideone.com/

Forward inference techniques such as sequential Monte Carlo and particle Markov chain Monte Carlo for probabilistic programming can be implemented in any programming language by creative use of standardized operating system functionality including processes, forking, mutexes, and shared memory. Exploiting this we have defined, developed, and tested a probabilistic programming language intermediate representation language we call probabilistic C, which itself can be compiled to machine code by standard compilers and linked to operating system libraries yielding an efficient, scalable, portable probabilistic programming compilation target. This opens up a new hardware and systems research path for optimizing probabilistic programming systems.

From the bitsavers.org collection, a scanned-in computer-related document. nbs :: publications :: NBS 500-117 Selection And Use Of General Purpose Programming Languages Vol1 Oct84

Episode 0002 of Will's Guide to Programming Languages, Programming, Computation, and What-Have-You.

Many applications require complexly structured data objects. Developing new or adapting existing algorithmic solutions for creating such objects can be a non-trivial and costly task if the considered objects are subject to different application-specific constraints. Often, however, it is comparatively easy to declaratively describe the required objects. In this paper, we propose to use answer-set programming (ASP)---a well-established declarative programming paradigm from the area of artificial intelligence---for instantiating objects in standard object-oriented programming languages. In particular, we extend Java with declarative specifications from which the required objects can be automatically generated using available ASP solver technology.

Many applications require complexly structured data objects. Developing new or adapting existing algorithmic solutions for creating such objects can be a non-trivial and costly task if the considered objects are subject to different application-specific constraints. Often, however, it is comparatively easy to declaratively describe the required objects. In this paper, we propose to use answer-set programming (ASP)---a well-established declarative programming paradigm from the area of artificial intelligence---for instantiating objects in standard object-oriented programming languages. In particular, we extend Java with declarative specifications from which the required objects can be automatically generated using available ASP solver technology.

Many applications require complexly structured data objects. Developing new or adapting existing algorithmic solutions for creating such objects can be a non-trivial and costly task if the considered objects are subject to different application-specific constraints. Often, however, it is comparatively easy to declaratively describe the required objects. In this paper, we propose to use answer-set programming (ASP)---a well-established declarative programming paradigm from the area of artificial intelligence---for instantiating objects in standard object-oriented programming languages. In particular, we extend Java with declarative specifications from which the required objects can be automatically generated using available ASP solver technology.

Many applications require complexly structured data objects. Developing new or adapting existing algorithmic solutions for creating such objects can be a non-trivial and costly task if the considered objects are subject to different application-specific constraints. Often, however, it is comparatively easy to declaratively describe the required objects. In this paper, we propose to use answer-set programming (ASP)---a well-established declarative programming paradigm from the area of artificial intelligence---for instantiating objects in standard object-oriented programming languages. In particular, we extend Java with declarative specifications from which the required objects can be automatically generated using available ASP solver technology.

Many applications require complexly structured data objects. Developing new or adapting existing algorithmic solutions for creating such objects can be a non-trivial and costly task if the considered objects are subject to different application-specific constraints. Often, however, it is comparatively easy to declaratively describe the required objects. In this paper, we propose to use answer-set programming (ASP)---a well-established declarative programming paradigm from the area of artificial intelligence---for instantiating objects in standard object-oriented programming languages. In particular, we extend Java with declarative specifications from which the required objects can be automatically generated using available ASP solver technology.

Many applications require complexly structured data objects. Developing new or adapting existing algorithmic solutions for creating such objects can be a non-trivial and costly task if the considered objects are subject to different application-specific constraints. Often, however, it is comparatively easy to declaratively describe the required objects. In this paper, we propose to use answer-set programming (ASP)---a well-established declarative programming paradigm from the area of artificial intelligence---for instantiating objects in standard object-oriented programming languages. In particular, we extend Java with declarative specifications from which the required objects can be automatically generated using available ASP solver technology.

Many applications require complexly structured data objects. Developing new or adapting existing algorithmic solutions for creating such objects can be a non-trivial and costly task if the considered objects are subject to different application-specific constraints. Often, however, it is comparatively easy to declaratively describe the required objects. In this paper, we propose to use answer-set programming (ASP)---a well-established declarative programming paradigm from the area of artificial intelligence---for instantiating objects in standard object-oriented programming languages. In particular, we extend Java with declarative specifications from which the required objects can be automatically generated using available ASP solver technology.

Many applications require complexly structured data objects. Developing new or adapting existing algorithmic solutions for creating such objects can be a non-trivial and costly task if the considered objects are subject to different application-specific constraints. Often, however, it is comparatively easy to declaratively describe the required objects. In this paper, we propose to use answer-set programming (ASP)---a well-established declarative programming paradigm from the area of artificial intelligence---for instantiating objects in standard object-oriented programming languages. In particular, we extend Java with declarative specifications from which the required objects can be automatically generated using available ASP solver technology.

Many applications require complexly structured data objects. Developing new or adapting existing algorithmic solutions for creating such objects can be a non-trivial and costly task if the considered objects are subject to different application-specific constraints. Often, however, it is comparatively easy to declaratively describe the required objects. In this paper, we propose to use answer-set programming (ASP)---a well-established declarative programming paradigm from the area of artificial intelligence---for instantiating objects in standard object-oriented programming languages. In particular, we extend Java with declarative specifications from which the required objects can be automatically generated using available ASP solver technology.

Most integrations of artificial intelligence (AI) capabilities with non-AI (usually FORTRAN-based) application programs require the latter to execute separately to run as a subprogram or, at best, as a coroutine, of the AI system. In many cases, this organization is unacceptable; instead, the requirement is for an AI facility that runs in embedded mode; i.e., is called as subprogram by the application program. The design and implementation of a Prolog-based AI capability that can be invoked in embedded mode are described. The significance of this system is twofold: Provision of Prolog-based symbol-manipulation and deduction facilities makes a powerful symbolic reasoning mechanism available to applications programs written in non-AI languages. The power of the deductive and non-procedural descriptive capabilities of Prolog, which allow the user to describe the problem to be solved, rather than the solution, is to a large extent vitiated by the absence of the standard control structures provided by other languages. Embedding invocations of Prolog rule bases in programs written in non-AI languages makes it possible to put Prolog calls inside DO loops and similar control constructs. The resulting merger of non-AI and AI languages thus results in a symbiotic system in which the advantages of both programming systems are retained, and their deficiencies largely remedied.

Programming languages have undergone significant evolution, profoundly influencing the digital world. This article explores their history, classification, key features, and future trends. We examine the transition from low-level to high-level languages, the emergence of object-oriented and functional programming paradigms, and the influence of new technologies like AI and quantum computing on language design. A thorough understanding of the principles and trends in programming languages empowers developers to remain at the forefront of innovation and create cutting-edge software solutions.

Perma.cc archive of https://www.blockchain-council.org/blockchain/best-programming-languages-to-build-smart-contracts/ created on 2022-10-01 18:21:07.974691+00:00.

Perma.cc archive of https://builtin.com/data-science/data-science-programming-languages created on 2021-11-12 15:40:34+00:00.

A textbook on programming languages including basic concepts, object-oriented programming, logic programming and functional programming.

Includes bibliographies and index

Includes bibliographies and index

Includes bibliographies and index

1 online resource

1 online resource

Implementing Programming Languages (PLs) has always been a challenge for various reasons. One reason is the excess of routine tasks to be redone on every implementation cycle. This is despite the remarkable fraction of syntax and semantics usually shared between successive cycles. In this paper, we present a component-based approach to avoid reimplementation of shared PL fractions. We provide two sets of reusable components; one for syntax implementation and another for semantics. Our syntax and semantics components correspond to syntactic categories and semantics rules of a PL specification, respectively. We show how, in addition to their service to reusability in syntax and semantics, our components can cater reusable implementation of PL analyses. Our current level of experimentation suggests that this approach is applicable wherever the following two features are available or can be simulated: Type Constraints and Multiple Inheritance. Implementing a PL using our approach, however, requires some modest programming discipline that we will explain throughout the text.

Implementing Programming Languages (PLs) has always been a challenge for various reasons. One reason is the excess of routine tasks to be redone on every implementation cycle. This is despite the remarkable fraction of syntax and semantics usually shared between successive cycles. In this paper, we present a component-based approach to avoid reimplementation of shared PL fractions. We provide two sets of reusable components; one for syntax implementation and another for semantics. Our syntax and semantics components correspond to syntactic categories and semantics rules of a PL specification, respectively. We show how, in addition to their service to reusability in syntax and semantics, our components can cater reusable implementation of PL analyses. Our current level of experimentation suggests that this approach is applicable wherever the following two features are available or can be simulated: Type Constraints and Multiple Inheritance. Implementing a PL using our approach, however, requires some modest programming discipline that we will explain throughout the text.

Implementing Programming Languages (PLs) has always been a challenge for various reasons. One reason is the excess of routine tasks to be redone on every implementation cycle. This is despite the remarkable fraction of syntax and semantics usually shared between successive cycles. In this paper, we present a component-based approach to avoid reimplementation of shared PL fractions. We provide two sets of reusable components; one for syntax implementation and another for semantics. Our syntax and semantics components correspond to syntactic categories and semantics rules of a PL specification, respectively. We show how, in addition to their service to reusability in syntax and semantics, our components can cater reusable implementation of PL analyses. Our current level of experimentation suggests that this approach is applicable wherever the following two features are available or can be simulated: Type Constraints and Multiple Inheritance. Implementing a PL using our approach, however, requires some modest programming discipline that we will explain throughout the text.

Implementing Programming Languages (PLs) has always been a challenge for various reasons. One reason is the excess of routine tasks to be redone on every implementation cycle. This is despite the remarkable fraction of syntax and semantics usually shared between successive cycles. In this paper, we present a component-based approach to avoid reimplementation of shared PL fractions. We provide two sets of reusable components; one for syntax implementation and another for semantics. Our syntax and semantics components correspond to syntactic categories and semantics rules of a PL specification, respectively. We show how, in addition to their service to reusability in syntax and semantics, our components can cater reusable implementation of PL analyses. Our current level of experimentation suggests that this approach is applicable wherever the following two features are available or can be simulated: Type Constraints and Multiple Inheritance. Implementing a PL using our approach, however, requires some modest programming discipline that we will explain throughout the text.

Implementing Programming Languages (PLs) has always been a challenge for various reasons. One reason is the excess of routine tasks to be redone on every implementation cycle. This is despite the remarkable fraction of syntax and semantics usually shared between successive cycles. In this paper, we present a component-based approach to avoid reimplementation of shared PL fractions. We provide two sets of reusable components; one for syntax implementation and another for semantics. Our syntax and semantics components correspond to syntactic categories and semantics rules of a PL specification, respectively. We show how, in addition to their service to reusability in syntax and semantics, our components can cater reusable implementation of PL analyses. Our current level of experimentation suggests that this approach is applicable wherever the following two features are available or can be simulated: Type Constraints and Multiple Inheritance. Implementing a PL using our approach, however, requires some modest programming discipline that we will explain throughout the text.

In order to promote public education and public safety, equal justice for all, a better informed citizenry, the rule of law, world trade and world peace, this legal document is hereby made available on a noncommercial basis, as it is the right of all humans to know and speak the laws that govern them. (For more information: 12 Tables of Code ) Name of Standards Organization: Bureau of Indian Standards (BIS) Division Name: Electronics and Information Technology Section Name: Software and System Engineering (LITD 14) Designator of Legally Binding Document: IS 13791 Title of Legally Binding Document: Programming languages - PL/I Number of Amendments: Equivalence: ISO 6160 Superceding: Superceded by: LEGALLY BINDING DOCUMENT Step Out From the Old to the New --Jawaharlal Nehru Invent a new India using knowledge. --Satyanarayan Gangaram Pitroda

ACM Transactions on Programming Languages and Systems 1987: Volume 9 , Issue Index. Digitized from IA1534625-06 . Previous issue: sim_acm-transactions-on-programming-languages-and-systems_1986-10_8_4 . Next issue: sim_acm-transactions-on-programming-languages-and-systems_1987-01_9_1 .

ACM Transactions on Programming Languages and Systems 1987: Volume 9 , Issue Index. Digitized from IA1534625-06 . Previous issue: sim_acm-transactions-on-programming-languages-and-systems_1986-10_8_4 . Next issue: sim_acm-transactions-on-programming-languages-and-systems_1987-01_9_1 .

In order to promote public education and public safety, equal justice for all, a better informed citizenry, the rule of law, world trade and world peace, this legal document is hereby made available on a noncommercial basis, as it is the right of all humans to know and speak the laws that govern them. (For more information: 12 Tables of Code ) Name of Standards Organization: Bureau of Indian Standards (BIS) Division Name: Electronics and Information Technology Section Name: Software and System Engineering (LITD 14) Designator of Legally Binding Document: IS 13792 Title of Legally Binding Document: Programming languages - ADA Number of Amendments: Equivalence: ISO 8652 Superceding: Superceded by: LEGALLY BINDING DOCUMENT Step Out From the Old to the New --Jawaharlal Nehru Invent a new India using knowledge. --Satyanarayan Gangaram Pitroda

From the bitsavers.org collection, a scanned-in computer-related document. xerox :: parc :: techReports :: CSL-81-11 Real Programming in Functional Languages

"NPS-52-81-009."

"NPS-52-81-009."

"NPS-52-81-009."

"NPS-52-81-009."

Abstract This survey examines thirty-five parallel programming languages and fifty-nine parallel programming tools. Focus is placed on those tool capabilities needed for parallel scientific programming rather than for general computer science. The tools are classified based on their functions and ranked with current and future needs of NAS in mind. In particular, existing and anticipated NAS supercomputers and workstations, operating systems, programming languages and applications. Tables are presented to compare the main functions provided by the tools.

Video content can be found here: https://www.youtube.com/channel/UC0BAd8tPlDqFvDYBemHcQPQ/

Scientific computation is a discipline that combines numerical analysis, physical understanding, algorithm development, and structured programming. Several yottacycles per year on the world's largest computers are spent simulating problems as diverse as weather prediction, the properties of material composites, the behavior of biomolecules in solution, and the quantum nature of chemical compounds. This article is intended to review specfic languages features and their use in computational science. We will review the strengths and weaknesses of different programming styles, with examples taken from widely used scientific codes.

From the bitsavers.org collection, a scanned-in computer-related document. apple :: mac :: a ux :: aux 1.0 :: AUX 1.0 Programming Languages and Tools Volume 1 1987

The push button telephone has been employed in many systems as a computer terminal with a very restricted capability and in at least one instance as a computer terminal for a general purpose programming language This thesis discusses the input, output, coding, and decoding problems when considering a general purpose programming language for use with a push button telephone,, Included is a general discussion of a system that would use a syntactic analysis and the context of a programming language to produce a code for use with a push button telephone. The output from this analysis would be used to build a table-driven translator to decode the language An example of such a system is also included.

The push button telephone has been employed in many systems as a computer terminal with a very restricted capability and in at least one instance as a computer terminal for a general purpose programming language This thesis discusses the input, output, coding, and decoding problems when considering a general purpose programming language for use with a push button telephone,, Included is a general discussion of a system that would use a syntactic analysis and the context of a programming language to produce a code for use with a push button telephone. The output from this analysis would be used to build a table-driven translator to decode the language An example of such a system is also included.

The push button telephone has been employed in many systems as a computer terminal with a very restricted capability and in at least one instance as a computer terminal for a general purpose programming language This thesis discusses the input, output, coding, and decoding problems when considering a general purpose programming language for use with a push button telephone,, Included is a general discussion of a system that would use a syntactic analysis and the context of a programming language to produce a code for use with a push button telephone. The output from this analysis would be used to build a table-driven translator to decode the language An example of such a system is also included.

Programming languages are engineered languages that allow to instruct a machine and share algorithmic information; they have a great influence on the society since they underlie almost every information technology artefact, and they are at the core of the current explosion of software technology. The history of programming languages is marked by innovations, diversifications, lateral transfers and social influences; moreover, it represents an intermediate case study between the evolution of human languages and the evolution of technology. In this paper we study the application of the Darwinian explanation to the programming languages evolution by discussing to what extent the evolutionary mechanisms distinctive of biology can be applied to this area. We show that a number of evolutionary building blocks can be recognised in the realm of computer languages, but we also identify critical issues. Far from being crystal clear, this fine-grained study shows to be a useful tool to assess recent results about programming languages phylogenies. Finally, we show that rich evolutionary patterns, such as co-evolution, macro-evolutionary trends, niche construction and exaptation, can be effectively applied to programming languages and provide for interesting explanatory tools.