scholarly journals Meaning and Context in Computer Programs

Queue ◽  
2021 ◽  
Vol 19 (5) ◽  
pp. 60-68
Author(s):  
Alvaro Videla
Keyword(s):  
Domain Knowledge ◽  
Source Code ◽  
Computer Programs

When you look at a function program's source code, how do you know what it means? Is the meaning found in the return values of the function, or is it located inside the function body? What about the function name? Answering these questions is important to understanding how to share domain knowledge among programmers using the source code as the medium. The program is the medium of communication among programmers to share their solutions.

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 DeepAM: Migrate APIs with Multi-modal Sequence to Sequence Learning

2017 ◽  
Author(s):  
Xiaodong Gu ◽  
Hongyu Zhang ◽  
Dongmei Zhang ◽  
Sunghun Kim
Keyword(s):  
Sequence Learning ◽  
Large Scale ◽  
Intelligent System ◽  
State Of The Art ◽  
Source Code ◽  
Computer Programs ◽  
Experimental Results ◽  
Multiple Devices ◽  
Application Programming ◽  
Programming Interfaces

Computer programs written in one language are often required to be ported to other languages to support multiple devices and environments. When programs use language specific APIs (Application Programming Interfaces), it is very challenging to migrate these APIs to the corresponding APIs written in other languages. Existing approaches mine API mappings from projects that have corresponding versions in two languages. They rely on the sparse availability of bilingual projects, thus producing a limited number of API mappings. In this paper, we propose an intelligent system called DeepAM for automatically mining API mappings from a large-scale code corpus without bilingual projects. The key component of DeepAM is based on the multi-modal sequence to sequence learning architecture that aims to learn joint semantic representations of bilingual API sequences from big source code data. Experimental results indicate that DeepAM significantly increases the accuracy of API mappings as well as the number of API mappings when compared with the state-of-the-art approaches.

Jurnal PERANGKAT LUNAK KOMPUTER Ayubi Simatupang PMM-1

2020 ◽  
Author(s):  
S Mukhtar Ayubi Simatupang
Keyword(s):  
Programming Languages ◽  
Programming Language ◽  
Source Code ◽  
Computer Software ◽  
Computer Programs ◽  
Computer Hardware ◽  
Executable Programs

Abstrak- Perangkat lunak komputer atau yang sering disebut sebagai (software) mempunyai sifat yang berbeda dengan (hardware) atau perangkat keras komputer. Jika perangkat keras komputer dapat dilihat dan disentuh keberadaannya maka perangkat lunak pada suatu komputer hanya dapat dilihat saja tanpa dapat kita rasa atau raba bendanya. Lebih tepatnya, perangkat lunak tidak dapat disentuh dan memang secara fisik tidak tampak namun kita dapat mengoperasikannya. Namun walaupun tidak tampak secara fisik perangkat lunak sangat berguna dalam pengoperasiannya dengan adanya perangkat lunak suatu komputer dapat menjalankan suatu perintah. 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).Kata Kunci: Software, ProgrammerAbstac t- Computer software or often referred to as (software) has different properties from (hardware) or computer hardware. If the computer hardware can be seen and touched, then the software on a computer can only be seen without our feeling or feeling. More precisely, the software cannot be touched and it is physically invisible but we can operate it. But even though the software does not appear physically very useful in its operation with the software a computer can run a command. 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).Keywords: Software, Programmer

PERANGKAT LUNAK KOMPUTER

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

USING DOMAIN KNOWLEDGE TO IMPROVE REVERSE ENGINEERING

1996 ◽  
Vol 06 (03) ◽  
pp. 477-505 ◽  
Author(s):  
HARALD C. GALL ◽  
RENÉ R. KLÖSCH ◽  
ROLAND T. MITTERMEIR
Keyword(s):  
Reverse Engineering ◽  
Program Transformation ◽  
Domain Knowledge ◽  
Source Code ◽  
Object Oriented ◽  
Transformation Process ◽  
Legacy Systems ◽  
General Domain ◽  
Basic Concepts ◽  
Architectural Transformation

Integrating application domain knowledge into reverse engineering is an important step to overcome the shortcomings of conventional reverse engineering approaches that are based exclusively on information derivable from source code. In this paper, we show the basic concepts of a program transformation process from a conventional to an object-oriented architecture which incorporates extraneous higher-level knowledge in its process. To which degree this knowledge might stem from some general domain knowledge, and to which extent it needs to be introduced as application dependent knowledge by a human expert is discussed. The paper discusses these issues in the context of the architectural transformation of legacy systems to an object-oriented architecture.

scholarly journals SEMANTIC APPROACH TO SMART CONTRACT VERIFICATION

2020 ◽  
Vol 19 (1) ◽  
pp. 021
Author(s):  
Nenad Petrović ◽  
Milorad Tošić
Keyword(s):  
Domain Knowledge ◽  
Expert Knowledge ◽  
Semantic Representation ◽  
Formal Semantics ◽  
Source Code ◽  
Limiting Factors ◽  
Smart Contracts ◽  
Semantic Approach ◽  
Blockchain Technology ◽  
Smart Contract

Vulnerabilities of smart contract are certainly one of the limiting factors for wider adoption of blockchain technology. Smart contracts written in Solidity language are considered due to common adoption of the Ethereum blockchain platform. Despite its popularity, the semantics of the language is not completely documented and relies on implicit mechanisms not publicly available and as such vulnerable to possible attacks. In addition, creating formal semantics for the higher-level language provides support to verification mechanisms. In this paper, a novel approach to smart contact verification is presented that uses ontologies in order to leverage semantic annotations of the smart contract source code combined with semantic representation of domain-specific aspects. The following aspects of smart contracts, apart from source code are taken into consideration for verification: business logic, domain knowledge, run-time state changes and expert knowledge about vulnerabilities. Main advantages of the proposed verification approach are platform independence and extendability.

scholarly journals Recent Advances in Intelligent Source Code Generation: A Survey on Natural Language Based Studies

Entropy ◽  
2021 ◽  
Vol 23 (9) ◽  
pp. 1174
Author(s):  
Chen Yang ◽  
Yan Liu ◽  
Changqing Yin
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Code Generation ◽  
Domain Knowledge ◽  
Large Scale ◽  
Source Code ◽  
Research Field ◽  
Machine Learning Algorithms ◽  
Transformation Model ◽  
Essential Components

Source Code Generation (SCG) is a prevalent research field in the automation software engineering sector that maps specific descriptions to various sorts of executable code. Along with the numerous intensive studies, diverse SCG types that integrate different scenarios and contexts continue to emerge. As the ultimate purpose of SCG, Natural Language-based Source Code Generation (NLSCG) is growing into an attractive and challenging field, as the expressibility and extremely high abstraction of the input end. The booming large-scale dataset generated by open-source code repositories and Q&A resources, the innovation of machine learning algorithms, and the development of computing capacity make the NLSCG field promising and give more opportunities to the model implementation and perfection. Besides, we observed an increasing interest stream of NLSCG relevant studies recently, presenting quite various technical schools. However, many studies are bound to specific datasets with customization issues, producing occasional successful solutions with tentative technical methods. There is no systematic study to explore and promote the further development of this field. We carried out a systematic literature survey and tool research to find potential improvement directions. First, we position the role of NLSCG among various SCG genres, and specify the generation context empirically via software development domain knowledge and programming experiences; second, we explore the selected studies collected by a thoughtfully designed snowballing process, clarify the NLSCG field and understand the NLSCG problem, which lays a foundation for our subsequent investigation. Third, we model the research problems from technical focus and adaptive challenges, and elaborate insights gained from the NLSCG research backlog. Finally, we summarize the latest technology landscape over the transformation model and depict the critical tactics used in the essential components and their correlations. This research addresses the challenges of bridging the gap between natural language processing and source code analytics, outlines different dimensions of NLSCG research concerns and technical utilities, and shows a bounded technical context of NLSCG to facilitate more future studies in this promising area.

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