The Long Tail: Understanding the Discoverability of API Functionality
| Autor: | Brad A. Myers, Mary Beth Kery, Mariann Nagy, Sachin Grover, Finn Voichick, Emily Zhou, Amber Horvath, Daye Nam, Sihan Dong, Shwetha Shinju |
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| Rok vydání: | 2019 |
| Předmět: |
Application programming interface
Computer science business.industry 05 social sciences Software development 020207 software engineering Usability 02 engineering and technology Python (programming language) Discoverability Documentation 0202 electrical engineering electronic engineering information engineering Task analysis 0501 psychology and cognitive sciences Software engineering business Programmer computer 050107 human factors computer.programming_language |
| Zdroj: | VL/HCC |
| DOI: | 10.1109/vlhcc.2019.8818681 |
| Popis: | Almost all software development revolves around the discovery and use of application programming interfaces (APIs). Once a suitable API is selected, programmers must begin the process of determining what functionality in the API is relevant to a programmer's task and how to use it. Our work aims to understand how API functionality is discovered by programmers and where tooling may be appropriate. We employed a mixed-methods approach to investigate Apache Beam, a distributed data processing API, by mining Beam client code and running a lab study to see how people discover Beam's available functionality. We found that programmers’ prior experience with similar APIs significantly impacted their ability to find relevant features in an API and attempting to form a top-down mental model of an API resulted in less discovery of features. |
| Databáze: | OpenAIRE |
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