A framework for analyzing and transforming source code supporting multiple programming languages

2013 ◽  
Author(s):  
Kazunori Sakamoto
Keyword(s):  
Programming Languages ◽  
Source Code

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 GERADOR DE CÓDIGOS PARA O DESENVOLVIMENTO DE APLICAÇÕES WEB A PARTIR DA MODELAGEM ENTIDADE-RELACIONAMENTO

e-xacta ◽  
2016 ◽  
Vol 9 (1) ◽  
pp. 37
Author(s):  
Cristiano Martins Monteiro ◽  
Flavianne Braga Campos de Lima ◽  
Carlos Renato Storck
Keyword(s):  
Programming Languages ◽  
Code Generation ◽  
Design Pattern ◽  
Design Patterns ◽  
Source Code ◽  
Automatic Generation ◽  
Code Generator ◽  
Routine Tasks ◽  
Entity Relationship ◽  
Web Systems

<p>A geração automática de código-fonte é uma prática adotada no desenvolvimento de softwares para agilizar, facilitar e padronizar a implementação dos projetos. Embora seja uma prática comum nas fábricas de software, não se conhece uma ferramenta que permita escolher o padrão de projeto a ser usado. O objetivo principal deste trabalho é apresentar um gerador de códigos para o desenvolvimento de sistemas Web a partir de uma modelagem entidade-relacionamento, uma linguagem de programação e um padrão de projeto determinados pelo usuário. Os objetivos específicos são propor uma arquitetura do sistema capaz de adequar e reaproveitar diferentes padrões de projeto, linguagens de programação e projetos cadastrados; permitir que o usuário cadastre, altere, exclua, importe e exporte um projeto; e gerar automaticamente o seu código-fonte e scripts de banco de dados. Este trabalho se justifica pela importância de reduzir erros de codificação; e evitar perca tempo ao realizar atividades rotineiras de implementação de padrões de projeto. Possibilitando assim, maior dedicação no planejamento das regras de negócios e redução de custos. A ferramenta proposta (GCER) foi desenvolvida em linguagem Java com o uso banco de dados Oracle 11g, e seguindo os padrões DAO e MVC. Os resultados foram avaliados através da geração e compilação de códigos de um projeto para cadastro de veículos. A geração com êxito evidencia a viabilidade da ferramenta proposta para a geração automática de códigos no processo de desenvolvimento de software.</p><p>Abstract</p><p>The automatic generation of source code is a practice adopted in the development of software to streamline, facilitate and standardize the implementation of projects. Although it be a common practice in software factories, it is not known a tool able to choose the design pattern to be used. The main objective of this paper is to present a code generator for the development of Web systems from an entity-relationship modeling, a programming language and a design pattern determined by the user. The specific objectives are to propose a system architecture able to suit and reuse different design patterns, programming languages and saved projects; allow the user to insert, update, delete, import and export a project; and automatically generate the source code and database scripts. This work is justified by the importance to reduce errors of coding; and to avoid waste of time in the development of Web systems performing routine tasks. Allowing, then, a greater dedication in the planning of business rules and the reduction of costs. The tool proposed (GCER) was developed in Java with the database using Oracle 11g, and following the DAO and MVC patterns. The results were evaluated by generating and compiling codes of a project for vehicle registration. The successful code generation demonstrate the feasibility of the proposed tool for the automatic generation of code in the software development process.</p>

scholarly journals Measuring source code conciseness across programming languages using compression

2021 ◽  
Author(s):  
Lodewijk Bergmans ◽  
Xander Schrijen ◽  
Edwin Ouwehand ◽  
Magiel Bruntink
Keyword(s):  
Programming Languages ◽  
Source Code

scholarly journals End-to-End Prediction of Buffer Overruns from Raw Source Code via Neural Memory Networks

2017 ◽  
Author(s):  
Min-je Choi ◽  
Sehun Jeong ◽  
Hakjoo Oh ◽  
Jaegul Choo
Keyword(s):  
Programming Languages ◽  
Program Analysis ◽  
Question Answering ◽  
Source Code ◽  
Source Codes ◽  
Program Language ◽  
Proposed Model ◽  
Challenging Tasks ◽  
Data Driven Approach ◽  
End To End

Detecting buffer overruns from a source code is one of the most common and yet challenging tasks in program analysis. Current approaches based on rigid rules and handcrafted features are limited in terms of flexible applicability and robustness due to diverse bug patterns and characteristics existing in sophisticated real-world software programs. In this paper, we propose a novel, data-driven approach that is completely end-to-end without requiring any hand-crafted features, thus free from any program language-specific structural limitations. In particular, our approach leverages a recently proposed neural network model called memory networks that have shown the state-of-the-art performances mainly in question-answering tasks. Our experimental results using source code samples demonstrate that our proposed model is capable of accurately detecting different types of buffer overruns. We also present in-depth analyses on how a memory network can learn to understand the semantics in programming languages solely from raw source codes, such as tracing variables of interest, identifying numerical values, and performing their quantitative comparisons.

scholarly journals COMPARISON OF SOFTWARE COMPLEXITY OF SEARCH ALGORITHM USING CODE BASED COMPLEXITY METRICS

2021 ◽  
Vol 6 (5) ◽  
Author(s):  
Bello Muriana ◽  
Ogba Paul Onuh
Keyword(s):  
Software Engineering ◽  
Programming Languages ◽  
Search Algorithm ◽  
Source Code ◽  
Search Algorithms ◽  
Binary Search ◽  
Software Systems ◽  
Software Complexity ◽  
Complexity Metrics ◽  
Binary Search Algorithm

Measures of software complexity are essential part of software engineering. Complexity metrics can be used to forecast key information regarding the testability, reliability, and manageability of software systems from study of the source code. This paper presents the results of three distinct software complexity metrics that were applied to two searching algorithms (Linear and Binary search algorithm). The goal is to compare the complexity of linear and binary search algorithms implemented in (Python, Java, and C++ languages) and measure the sample algorithms using line of code, McCabe and Halstead metrics. The findings indicate that the program difficulty of Halstead metrics has minimal value for both linear and binary search when implemented in python. Analysis of Variance (ANOVA) was adopted to determine whether there is any statistically significant differences between the search algorithms when implemented in the three programming languages and it was revealed that the three (3) programming languages do not vary considerably for both linear and binary search techniques which implies that any of the (3) programming languages is suitable for coding linear and binary search algorithms.

scholarly journals Code Completion with Neural Attention and Pointer Networks

2018 ◽  
Author(s):  
Jian Li ◽  
Yue Wang ◽  
Michael R. Lyu ◽  
Irwin King
Keyword(s):  
Software Development ◽  
Programming Languages ◽  
Source Code ◽  
Attention Mechanism ◽  
Language Models ◽  
Local Context ◽  
Completion Task ◽  
Code Completion ◽  
Research Task ◽  
Vocabulary Word

Intelligent code completion has become an essential research task to accelerate modern software development. To facilitate effective code completion for dynamically-typed programming languages, we apply neural language models by learning from large codebases, and develop a tailored attention mechanism for code completion. However, standard neural language models even with attention mechanism cannot correctly predict the out-of-vocabulary (OoV) words that restrict the code completion performance. In this paper, inspired by the prevalence of locally repeated terms in program source code, and the recently proposed pointer copy mechanism, we propose a pointer mixture network for better predicting OoV words in code completion. Based on the context, the pointer mixture network learns to either generate a within-vocabulary word through an RNN component, or regenerate an OoV word from local context through a pointer component. Experiments on two benchmarked datasets demonstrate the effectiveness of our attention mechanism and pointer mixture network on the code completion task.

scholarly journals Enhancing the Unified Features to Locate Buggy Files by Exploiting the Sequential Nature of Source Code

2017 ◽  
Author(s):  
Xuan Huo ◽  
Ming Li
Keyword(s):  
Programming Languages ◽  
Software Maintenance ◽  
Structural Information ◽  
Source Code ◽  
Semantic Features ◽  
Software Projects ◽  
Bug Reports ◽  
Bug Report ◽  
Structural Semantics ◽  
Sequential Nature

Bug reports provide an effective way for end-users to disclose potential bugs hidden in a software system, while automatically locating the potential buggy source files according to a bug report remains a great challenge in software maintenance. Many previous approaches represent bug reports and source code from lexical and structural information correlated their relevance by measuring their similarity, and recently a CNN-based model is proposed to learn the unified features for bug localization, which overcomes the difficulty in modeling natural and programming languages with different structural semantics. However, previous studies fail to capture the sequential nature of source code, which carries additional semantics beyond the lexical and structural terms and such information is vital in modeling program functionalities and behaviors. In this paper, we propose a novel model LS-CNN, which enhances the unified features by exploiting the sequential nature of source code. LS-CNN combines CNN and LSTM to extract semantic features for automatically identifying potential buggy source code according to a bug report. Experimental results on widely-used software projects indicate that LS-CNN significantly outperforms the state-of-the-art methods in locating buggy files.

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