MEASURING AND IMPROVING API USABILITY AND QUALITY: A COMPREHENSIVE FRAMEWORK AND EMPIRICAL STUDY

Abstract

Cloud computing provides on-demand access to flexible computing resources, enabling rapid application deployment without substantial infrastructure investment. Application Programming Interfaces (APIs) play an important role in ensuring the success of cloud applications. The primary users of APIs are the extensive community of application programmers who search, read, and understand APIs before integrating them into their applications or systems. In addition, developers often turn to online API support when seeking help. Problems in such support can result in incorrect API usage and integration problems. There is an urgent need to measure API usability and support issues to identify, characterize, and assess the risks associated with them, and to effectively address these challenges in order to enhance API usability and quality.

This dissertation presents a comprehensive framework for measuring and improving API usability and quality, consisting of a measurement framework and an analysis/modeling framework. The measurement framework establishes direct metrics for API usability and quality, focusing on learnability, user satisfaction, and support issues. It also defines a set of indirect metrics, including environmental, code, and documentation metrics. Our analysis/modeling framework empirically validates the measurement framework and establishes predictive relations between its two sets of measurements.

A case study on the YouTube APIs, a set of widely used public APIs, demonstrates the applicability and effectiveness of our framework. We define four specific direct metrics to quantify usability and support problems for these APIs, and 13 indirect metrics on the application environment, API code, and documentation. Using tree-based models (TBMs), we examine the complex, non-linear relationships between these two sets of metrics, and establish predictive relationships between them. Additionally, TBM analysis identifies high-risk areas in API usability and support, where specific factors and their values are linked to significantly longer response times, reduced user satisfaction, increased downvotes or API support issues. These findings offer actionable feedback for API providers, allowing them to enhance API usability and support by addressing critical areas that have been identified and characterized, ultimately fostering better developer experiences and wider API adoption.