@inproceedings{e766d92ae94e465e8bb140e8b25b1cba,
title = "Towards Validation of a Model of API Learning",
abstract = "APIs (Application Programming Interfaces) and code libraries have become highly integrated into the programming process. They allow programmers to reuse large segments of functionalities. However, as free and often open-source commodities, the support for programmers to learn how to use these valuable resources is not always complete. Researchers have repeatedly found that API learning is a highly problematic process with many barriers. However, much of the work on the difficulties using and learning APIs has relied on retrospective descriptions of the process or questions programmers post on forums. Furthermore, these explorations of difficulties in learning APIs have not taken into account theories about learning or information foraging. In this works-in-progress poster, we present an early evaluation of a model that describes API learning using both information foraging and cognitive load theory.",
keywords = "API learning, cognitive load, empirical study, external memory, information foraging theory, modeling, qualitative",
author = "Finn Voichick and Gao Gao and Michelle Ichinco and Caitlin Kelleher",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE Symposium on Visual Languages and Human-Centric Computing, VL/HCC 2019 ; Conference date: 14-10-2019 Through 18-10-2019",
year = "2019",
month = oct,
doi = "10.1109/VLHCC.2019.8818795",
language = "English",
series = "Proceedings of IEEE Symposium on Visual Languages and Human-Centric Computing, VL/HCC",
publisher = "IEEE Computer Society",
pages = "267--269",
editor = "Justin Smith and Bogart, \{Christopher A.\} and Judith Good and Fleming, \{Scott D.\}",
booktitle = "Proceedings - 2019 IEEE Symposium on Visual Languages and Human-Centric Computing, VL/HCC 2019",
}