The Case for API Communicability Evaluation: Introducing API-SI with Examples from Keras
This work addresses the problem of API design evaluation for a broad audience including non-professional programmers, though it is incremental as it builds on a previously proposed semiotic tool.
The paper tackles the need for APIs to communicate design intent effectively to both professional and non-professional users by introducing API Signification Inspection (API-SI), a semiotic evaluation tool, and demonstrates its application with the Keras Deep Learning API to complement usability studies.
In addition to their vital role in professional software development, Application Programming Interfaces (APIs) are now increasingly used by non-professional programmers, including end users, scientists and experts from other domains. Therefore, good APIs must meet old and new user requirements. Most of the re-search on API evaluation and design derives from user-centered, cognitive perspectives on human-computer interaction. As an alternative, we present a lower-threshold variant of a previously proposed semiotic API evaluation tool. We illustrate the procedures and power of this variant, called API Signification Inspection (API-SI), with Keras, a Deep Learning API. The illustration also shows how the method can complement and fertilize API usability studies. Additionally, API-SI is packaged as an introductory semiotic tool that API designers and researchers can use to evaluate the communication of design intent and product rationale to other programmers through implicit and explicit signs thereof, encountered in the API structure, behavior and documentation.