학술논문

Providing a labeled statements dataset to enhance the trans-compilation-based tools
Document Type
Conference
Source
2022 4th Novel Intelligent and Leading Emerging Sciences Conference (NILES) Novel Intelligent and Leading Emerging Sciences Conference (NILES), 2022 4th. :252-255 Oct, 2022
Subject
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Codes
Costs
Target recognition
Source coding
Operating systems
Entertainment industry
Companies
cross-platform
code conversion
code generation mobile applications
trans-compilation
data mining
machine learning
Language
Abstract
Nowadays Mobile Applications are a necessity as everyone is depending on them in their everyday tasks. We use them for communication, entertainment, and utilities. Every day new devices are introduced to the market. The diversity in these devices resulted in many platforms like Android and iOS. These different mobile platforms used by different companies and manufacturers made it challenging for mobile developersto build an application or a system that can be deployed on different platforms. Developers will be required to go through multiple development cycles to develop the application for each of the targeted platforms. To solve these issues, cross-platform frameworks emerged as a solution that would help the developers to write one codebase and deploy it on multiple platforms at the same time. Even though cross-platform frameworks reduced the time and cost needed to develop an application, but they did not provide the native feel and performance of the native applications. It was also challenging for developers to integrate cross-platform applications with a pre-existing system and to maintain the system. Some approaches were introduced to solve these issues by allowing the developer to write one codebase and generate the native code for each platform. One of the recently presented approaches is the trans-compilation approach that depends on the parser code to recognize the source code and generate the corresponding code for it in the target language. Even though the trans-compiler-based approaches were effective in providing an application that combines the advantages of the cross-platform and native development, it faced some problems in dealing with statements that is not expressed in the language's grammar rules, eternal APIs, and APIs that require code definition, like Firebase. In this paper, our aim is to provide a solution that can be integrated with the trans-compilation approach to solve the previously mentioned issues to enhance the conversion success rate by recognizing statements and predict the corresponding ones