Intelligent programming support system: machine learning feat. Fast development of secure programs.

N.E. Romanov, K. Izrailov, V. Pokussov
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引用次数: 1

Abstract

The article is devoted to the field of software development. The considered scientific contradiction lies in the fact that, on the one hand, the use of manual labor of a programmer is necessary in this area, and on the other hand, the presence of a human factor negatively affects the safety of the resulting code. To resolve the contradiction, it is proposed to use machine learning, which is traditionally used to solve the problem of classification, regression, search for anomalies, clustering, generalization and search for associations. It is shown that the majority of publications on this solution are of a private nature and do not cover the entire spectrum of possibilities. Various ways of automating the programming process using solutions for the specified machine learning problems are considered and substantiated. The demand for a system that combines such methods is indicated; Also, for the first time, its author’s definition is introduced: «Intelligent Programming Support System – a computer automated system based on artificial intelligence technologies, the purpose of which is to help developers of program code in the interests of reducing and simplifying manual labor, as well as increasing the safety of the final product». A comparative analysis of automation methods based on machine learning is given according to 8 criteria that this intelligent system must meet. The ways of further continuation of the research are indicated.
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智能编程支持系统:机器学习的壮举。安全程序的快速开发。
这篇文章专门讨论软件开发领域。考虑到的科学矛盾在于这样一个事实,一方面,程序员在这个领域中使用体力劳动是必要的,另一方面,人为因素的存在对结果代码的安全性产生了负面影响。为了解决这一矛盾,提出使用机器学习,传统上使用机器学习来解决分类、回归、异常搜索、聚类、泛化和关联搜索等问题。结果表明,关于这一解决方案的大多数出版物都属于私人性质,没有涵盖所有可能性。考虑并证实了使用特定机器学习问题的解决方案自动化编程过程的各种方法。指出了对结合这些方法的系统的需求;此外,首次介绍了其作者的定义:“智能编程支持系统-基于人工智能技术的计算机自动化系统,其目的是帮助程序代码的开发人员减少和简化体力劳动,并提高最终产品的安全性”。根据该智能系统必须满足的8个标准,对基于机器学习的自动化方法进行了比较分析。指出了进一步开展研究的途径。
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