基于MDA和卷积神经网络的代码生成系统
Gabriel Vargas-Monroy1, Daissi-Bibiana Gonzalez-Roldan1, Carlos Enrique Montenegro-Marín1
1Facultad de Ingeniería, Universidad Distrital Francisco José de Caldas, Bogotá, Colombia.
Frontiers in artificial intelligence
|March 26, 2025
概括
本研究介绍了一种使用计算机视觉和深度学习的新平台,可以从系统图表中自动生成源代码. 这简化了软件开发,提高了可扩展性和适应性.
科学领域:
- 计算机科学 计算机科学
- 软件工程 软件工程 软件工程
背景情况:
- 软件行业要求具有可扩展性和适应性的架构.
- 高效的开发流程对于高性能软件至关重要.
研究的目的:
- 通过连接规划,结构和编码来开发一个简化软件开发的平台.
- 使用先进的AI技术,从系统图表中生成源代码.
主要方法:
- 使用计算机视觉和深度学习用于图像处理和信息提取.
- 利用模型驱动架构 (MDA) 来从图表中生成源代码.
- 与Celery和Redis一起实施了一个模块化基础设施,用于异步任务管理.
主要成果:
- 证明了计算机视觉和深度学习在解释代码生成图表中的有效性.
- 通过使用MDA方法从图表中成功生成软件工件.
- 通过插件和域特定语言 (DSL) 增强灵活性,以支持多种语言和自动部署.
结论:
- 开发的系统有效地从图表中生成源代码,解决软件开发中的挑战.
- 计算机视觉,深度学习和MDA的整合为自动化软件工程提供了一个有前途的方法.
- 模块化设计和可扩展性确保了对各种平台的高效和适应性代码生成.
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