Abstract

The problem of technical debt arises when part of software source code is upgrading not directly, but is fixed in the second place as outdated. Three corresponding models are presented. Machine learning is used to find code smells. The effectiveness of the approach for specific data is established and the prospect of expanding to a greater number of different cases is outlined.

中文翻译：

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用于软件技术债务检测的机器学习

摘要

当部分软件源代码不是直接升级，而是因为过时而被修复时，就会出现技术债务问题。提出了三个相应的模型。机器学习用于发现代码异味。确定了该方法对特定数据的有效性，并概述了扩展到更多不同案例的前景。