支持程序-参数多态性的 C 语言扩展

IF 0.6 Q4 AUTOMATION & CONTROL SYSTEMS
A. I. Legalov, P. V. Kosov
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引用次数: 0

摘要

软件开发通常与扩展功能有关。在这种情况下,为了提高可靠性,有必要将先前编写的代码中的更改最小化。为了对程序的进化发展提供工具支持,提出了一种过程参数化编程范式,这使得增加过程化方法的能力成为可能。这样就可以轻松地扩展数据和函数。本文考虑在C语言中加入过程参数化程序设计。提出了额外的句法结构来支持所提出的方法。这些结构包括:参数化泛化、泛化的专门化、泛化函数和专门化处理程序。描述了它们的语义、可能性和技术实现的特征。为了检查使用这种方法的可能性,在C编程语言中建立了过程参数结构的模型。本文中的示例演示了程序的灵活扩展和对多态的支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
C Language Extension to Support Procedural-Parametric Polymorphism

Software development is often related to expanding functionality. To improve reliability in this case, it is necessary to minimize the change in the previously written code. For instrumental support of the evolutionary development of programs, a procedural-parametric programming paradigm is proposed, which makes it possible to increase the capabilities of the procedural approach. This allows extending both data and functions effortlessly. This paper considers the inclusion of procedural-parametric programming in the C language. Additional syntactic constructions are proposed to support the proposed approach. These constructions include: parametric generalizations, specializations of generalizations, generalizing functions, and specialization handlers. Their semantics, possibilities, and features of technical implementation are described. To check the possibilities of using this approach, models of procedural-parametric constructions in the C programming language are built. The example in this article demonstrates the flexible extension of the program and support of multiple polymorphism.

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来源期刊
AUTOMATIC CONTROL AND COMPUTER SCIENCES
AUTOMATIC CONTROL AND COMPUTER SCIENCES AUTOMATION & CONTROL SYSTEMS-
CiteScore
1.70
自引率
22.20%
发文量
47
期刊介绍: Automatic Control and Computer Sciences is a peer reviewed journal that publishes articles on• Control systems, cyber-physical system, real-time systems, robotics, smart sensors, embedded intelligence • Network information technologies, information security, statistical methods of data processing, distributed artificial intelligence, complex systems modeling, knowledge representation, processing and management • Signal and image processing, machine learning, machine perception, computer vision
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