scholarly journals Generalizations of Rice's Theorem, Applicable to Executable and Non-Executable Formalisms

10.29007/jnl6 ◽  
2018 ◽  
Author(s):  
Cornelis Huizing ◽  
Ruurd Kuiper ◽  
Tom Verhoeff
Keyword(s):  
Programming Languages ◽  
Programming Language ◽  
Specification Language ◽  
Partial Function ◽  
Program Text ◽  
Turing Machines ◽  
Partial Functions ◽  
Executable Specification ◽  
Special Cases ◽  
Diagonal Argument

We formulate and prove two Rice-like theoremsthat characterize limitations on nameability of propertieswithin a given naming scheme for partial functions.Such a naming scheme can, but need not be, an executable formalism.A programming language is an example of an executable naming scheme,where the program text names the partial function it implements.Halting is an example of a propertythat is not nameable in that naming scheme.The proofs reveal requirements on the naming schemeto make the characterization work.Universal programming languages satisfy these requirements,but also other formalisms can satisfy them.We present some non-universal programming languagesand a non-executable specification languagesatisfying these requirements.Our theorems haveTuring's well-known Halting Theorem and Rice's Theorem as special cases,by applying them to a universal programming language or Turing Machines as naming scheme.Thus, our proofs separate the nature of the naming scheme(which can, but need not, coincide with computability) from the diagonal argument.This sheds further light on how far reaching and simple the `diagonal' argument is in itself.

scholarly journals Deep Reinforcement Learning for Syntactic Error Repair in Student Programs

2019 ◽  
Vol 33 ◽  
pp. 930-937 ◽  
Author(s):  
Rahul Gupta ◽  
Aditya Kanade ◽  
Shirish Shevade
Keyword(s):  
Reinforcement Learning ◽  
Programming Languages ◽  
Programming Language ◽  
Online Course ◽  
Program Text ◽  
Neural Machine Translation ◽  
C Programs ◽  
Massive Open Online Course ◽  
Formal Syntax ◽  
Error Repair

Novice programmers often struggle with the formal syntax of programming languages. In the traditional classroom setting, they can make progress with the help of real time feedback from their instructors which is often impossible to get in the massive open online course (MOOC) setting. Syntactic error repair techniques have huge potential to assist them at scale. Towards this, we design a novel programming language correction framework amenable to reinforcement learning. The framework allows an agent to mimic human actions for text navigation and editing. We demonstrate that the agent can be trained through self-exploration directly from the raw input, that is, program text itself, without either supervision or any prior knowledge of the formal syntax of the programming language. We evaluate our technique on a publicly available dataset containing 6975 erroneous C programs with typographic errors, written by students during an introductory programming course. Our technique fixes 1699 (24.4%) programs completely and 1310 (18.8%) program partially, outperforming DeepFix, a state-of-the-art syntactic error repair technique, which uses a fully supervised neural machine translation approach.

The relation between logic programming and logic specification

1984 ◽  
Vol 312 (1522) ◽  
pp. 345-361 ◽  
Keyword(s):  
Logic Programming ◽  
Programming Languages ◽  
Programming Language ◽  
Specification Language ◽  
Formal Logic ◽  
Automated Deduction ◽  
Logic Programs ◽  
Computing Science ◽  
Complete Specification ◽  
Representation Of Knowledge

Formal logic is widely accepted as a program specification language in computing science. It is ideally suited to the representation of knowledge and the description of problems without regard to the choice of programming language. Its use as a specification language is compatible not only with conventional programming languages but also with programming languages based entirely on logic itself. In this paper I shall investigate the relation that holds when both programs and program specifications are expressed in formal logic. In many cases, when a specification completely defines the relations to be computed, there is no syntactic distinction between specification and program. Moreover the same mechanism that is used to execute logic programs, namely automated deduction, can also be used to execute logic specifications. Thus all relations defined by complete specifications are executable. The only difference between a complete specification and a program is one of efficiency. A program is more efficient than a specification.

The lazy logic of partial terms

2002 ◽  
Vol 67 (3) ◽  
pp. 1065-1077
Author(s):  
Raymond D. Gumb
Keyword(s):  
Computer Science ◽  
Programming Languages ◽  
Programming Language ◽  
Specific Area ◽  
The Other ◽  
Science Program ◽  
Free Logic ◽  
Partial Functions ◽  
Unary Function ◽  
Language Constructs

AbstractThe Logic of Partial TermsLPTis a strict negative free logic that provides an economical framework for developing many traditional mathematical theories having partial functions. In these traditional theories, all functions and predicates are strict. For example, if a unary function (predicate) is applied to an undefined argument, the result is undefined (respectively, false). On the other hand, every practical programming language incorporates at least one nonstrict or lazy construct, such as the if-then-else, but nonstrict functions cannot be either primitive or introduced in definitional extensions inLPT. Consequently, lazy programming language constructs do not fit the traditional mathematical mold inherent inLPT. A nonstrict (positive free) logic is required to handle nonstrict functions and predicates.Previously developed nonstrict logics are not fully satisfactory because they are verbose in describing strict functions (which predominate in many programming languages), and some logicians find their semantics philosophically unpalatable. The newly developed Lazy Logic of Partial TermsLLis as concise asLPTin describing strict functions and predicates, and strict and nonstrict functions and predicates can be introduced in definitional extensions of traditional mathematical theories.LLis “built on top of”LPT. and, likeLPT, admits only one domain in the semantics. In the semantics, for the case of a nonstrict unary functionhin an LL theoryT, we have ⊨Th(⊥) =y↔ ∀x(h(x) = y), where ⊥ is a canonical undefined term. Correspondingly, in the axiomatization, the “indifference” (to the value of the argument) axiomh(⊥) =y↔ ∀x(h(x) = y)guarantees a proper fit with the semantics. The price paid forLL's naturalness is that it is tailored for describing a specific area of computer science, program specification and verification, possibly limiting its role in explicating classical mathematical and philosophical subjects.

PERANGKAT LUNAK KOMPUTER

2020 ◽  
Author(s):  
Cut Nabilah Damni
Keyword(s):  
Programming Languages ◽  
Programming Language ◽  
Operating Systems ◽  
Source Code ◽  
Computer Software ◽  
Computer Programs ◽  
Application Systems ◽  
Executable Programs

AbstrakSoftware komputer atau perangkat lunak komputer merupakan kumpulan instruksi (program atau prosedur) untuk dapat melaksanakan pekerjaan secara otomatis dengan cara mengolah atau memproses kumpulan intruksi (data) yang diberikan. (Yahfizham, 2019 : 19) Sebagian besar dari software komputer dibuat oleh (programmer) dengan menggunakan bahasa pemprograman. Orang yang membuat bahasa pemprograman menuliskan perintah dalam bahasa pemprograman seperti layaknya bahasa yang digunakan oleh orang pada umumnya dalam melakukan perbincangan. Perintah-perintah tersebut dinamakan (source code). Program komputer lainnya dinamakan (compiler) yang digunakan pada (source code) dan kemudian mengubah perintah tersebut kedalam bahasa yang dimengerti oleh komputer lalu hasilnya dinamakan program executable (EXE). Pada dasarnya, komputer selalu memiliki perangkat lunak komputer atau software yang terdiri dari sistem operasi, sistem aplikasi dan bahasa pemograman.AbstractComputer software or computer software is a collection of instructions (programs or procedures) to be able to carry out work automatically by processing or processing the collection of instructions (data) provided. (Yahfizham, 2019: 19) Most of the computer software is made by (programmers) using the programming language. People who make programming languages write commands in the programming language like the language used by people in general in conducting conversation. The commands are called (source code). Other computer programs called (compilers) are used in (source code) and then change the command into a language understood by the computer and the results are called executable programs (EXE). Basically, computers always have computer software or software consisting of operating systems, application systems and programming languages.

scholarly journals Hybrid Java: The creation of a Hybrid Programming Environment

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Mark Noone ◽  
Aidan Mooney ◽  
Keith Nolan
Keyword(s):  
Programming Languages ◽  
Programming Language ◽  
Visual Programming ◽  
First Year ◽  
Programming Environment ◽  
Programming Environments ◽  
Hybrid Programming ◽  
Java Language ◽  
The Creation ◽  
Visual Programming Language

This article details the creation of a hybrid computer programming environment combining the power of the text-based Java language with the visual features of the Snap! language. It has been well documented that there exists a gap in the education of computing students in their mid-to-late teenage years, where perhaps visual programming languages are no longer suitable and textual programming languages may involve too steep of a learning curve. There is an increasing need for programming environments that combine the benefits of both languages into one. Snap! is a visual programming language which employs “blocks” to allow users to build programs, similar to the functionality offered by Scratch. One added benefit of Snap! is that it offers the ability to create one’s own blocks and extend the functionality of those blocks to create more complex and powerful programs. This will be utilised to create the Hybrid Java environment. The development of this tool will be detailed in the article, along with the motivation and use cases for it. Initial testing conducted will be discussed including one phase that gathered feedback from a pool of 174 first year Computer Science students. These participants were given instructions to work with the hybrid programming language and evaluate their experience of using it. The analysis of the findings along with future improvements to the language will also be presented.

scholarly journals Learners Programming Language a Helping System for Introductory Programming Courses

2016 ◽  
Vol 35 (3) ◽  
pp. 347-358
Author(s):  
Muhammad Shumail Naveed ◽  
Muhammad Sarim ◽  
Kamran Ahsan
Keyword(s):  
Programming Languages ◽  
Programming Language ◽  
Retention Rate ◽  
Introductory Programming ◽  
Source Program ◽  
Definition Of ◽  
High Level ◽  
Novice Students ◽  
Introductory Programming Courses ◽  
Programming Courses

Programming is the core of computer science and due to this momentousness a special care is taken in designing the curriculum of programming courses. A substantial work has been conducted on the definition of programming courses, yet the introductory programming courses are still facing high attrition, low retention and lack of motivation. This paper introduced a tiny pre-programming language called LPL (Learners Programming Language) as a ZPL (Zeroth Programming Language) to illuminate novice students about elementary concepts of introductory programming before introducing the first imperative programming course. The overall objective and design philosophy of LPL is based on a hypothesis that the soft introduction of a simple and paradigm specific textual programming can increase the motivation level of novice students and reduce the congenital complexities and hardness of the first programming course and eventually improve the retention rate and may be fruitful in reducing the dropout/failure level. LPL also generates the equivalent high level programs from user source program and eventually very fruitful in understanding the syntax of introductory programming languages. To overcome the inherent complexities of unusual and rigid syntax of introductory programming languages, the LPL provide elementary programming concepts in the form of algorithmic and plain natural language based computational statements. The initial results obtained after the introduction of LPL are very encouraging in motivating novice students and improving the retention rate.

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