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Towards a Model of API Learning

机译:迈向API学习的模型

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摘要

In today’s world, learning new APIs (Application Programming Interfaces) is fundamental to being a programmer. Prior research suggests that programmers learn on-the-fly while they work on other project-related tasks. Yet, this process is often inefficient. This inefficiency has inspired research seeking to understand and improve API learnability. While the existing research has provided insight into API learning, we still have a fractured understanding of the process of learning a new API. In this paper, we take the first steps towards developing a theoretical model of API learning by combining predictions from Information Foraging Theory (IFT) to describe information search behavior, Cognitive Load Theory (CLT) to describe learning, and External Memory (EM) to describe how API learners augment their short term memories. Our proposed model is consistent with existing research on barriers to learning APIs and helps to provide explanations for these barriers as well as suggest new research directions.
机译:在今天的世界中,学习新的API(应用程序编程接口)是作为程序员的基础。先前的研究表明,程序员在他们工作的其他与项目相关的任务中努力学习。然而,这个过程通常效率低下。这种效率效率激发了寻求理解和改善API可读性的研究。虽然现有的研究已经为API学习提供了深入了解,但我们仍然对学习新API的过程进行了破裂的理解。在本文中,我们采取第一个步骤通过组合信息觅食理论(IFT)的预测来描述API学习的理论模型来描述信息搜索行为,认知负载理论(CLT)来描述学习和外部存储器(EM)描述API学习者如何增加他们的短期记忆。我们拟议的模型与现有的学习API的障碍研究一致,并有助于为这些障碍提供解释,并提出新的研究方向。

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