ContextThe technical debt (TD) concept inspires the development of useful methods and tools that support TD identification and management. However, there is a lack of evidence on how different TD identification tools could be complementary and, also, how human-based identification compares with them.ObjectiveTo understand how to effectively elicit TD from humans, to investigate several types of tools for TD identification, and to understand the developers’ point of view about TD indicators and items reported by tools.MethodWe asked developers to identify TD items from a real software project. We also collected the output of three tools to automatically identify TD and compared the results in terms of their locations in the source code. Then, we collected developers’ opinions on the identification process through a focus group.ResultsAggregation seems to be an appropriate way to combine TD reported by developers. The tools used cannot help in identifying many important TD types, so involving humans is necessary. Developers reported that the tools would help them to identify TD faster or more accurately and that project priorities and current development activities are important to be considered together, along with the values of principal and interest, when deciding to pay off a debt.ConclusionThis work contributes to the TD landscape, which depicts an understanding between different TD types and how they are best discovered.

中文翻译：

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了解自动化和基于人的技术债务识别方法——一个两阶段的研究

背景技术债务 (TD) 概念激发了支持 TD 识别和管理的有用方法和工具的开发。然而，缺乏证据表明不同的TD识别工具如何互补，以及基于人类的识别如何与它们进行比较。目的了解如何有效地从人类中引出TD，研究几种类型的TD识别工具，并了解开发人员对工具报告的TD指标和项目的看法。方法我们要求开发人员从一个真实的软件项目中识别TD项目。我们还收集了三个工具的输出来自动识别 TD，并根据它们在源代码中的位置来比较结果。然后，我们通过焦点小组收集了开发人员对识别过程的意见。ResultsAggregation 似乎是结合开发人员报告的 TD 的合适方法。使用的工具无法帮助识别许多重要的 TD 类型，因此需要人类参与。开发人员报告说，这些工具将帮助他们更快或更准确地识别 TD，并且在决定偿还债务时，项目优先级和当前的开发活动以及本金和利息的价值很重要。结论这项工作有助于TD 景观，它描述了不同 TD 类型之间的理解以及如何最好地发现它们。