Designing and Evaluating the Usability of a Machine Learning API for Rapid Prototyping Music Technology - Bernardo, Francisco; Zbyszyński, Michael; Grierson, Mick; Fiebrink, Rebecca (Frontiers in Artificial Intelligence, 2020)
To better support creative software developers and music technologists’ needs, and to empower them as machine learning users and innovators, the usability of and developer experience with machine learning tools must be considered and better understood. This paper presents a usability evaluation study of an application programming interface for interactive machine learning with software developers of music technology. A cognitive dimensions questionnaire was designed and delivered to a group of 12 participants who used the RAPID-MIX API in their software projects, including people who developed systems for personal use and professionals developing software products for music and creative technology companies. Based on the findings obtained through the cognitive dimensions framework, we present an analysis and characterisation of an ML API and discuss its design trade-offs and usability issues. Additionally, we discuss the merits and challenges of the application of the cognitive dimensions framework to the evaluation of machine learning APIs.