AI-Enhanced API Design: A New Paradigm in Usability and Efficiency
Abstract
This study uses mixed methods to evaluate API design methods, focusing on design and consumption phases. Our goal was to understand the impact of API governance approaches on productivity and usability. A controlled developer experiment (n=34) demonstrated
a 10% increased requirement fulfillment using API Improvement Proposals (AIPs) and linter versus no protocols. Meanwhile, 73% of 33 surveyed API consumers preferred AIP-aligned designs for enhanced usability and comprehensibility. Complementing this, a
custom large language model called the API Architect received average expert ratings of just 5/10 for specification quality, revealing gaps versus manual design. The quantitative performance metrics combined with qualitative user feedback provide evidence from
multiple angles that strategically integrating industry best practices with maturing AI capabilities can meaningfully improve API design outcomes. This research offers empirical insights from developer and consumer perspectives to advance scholarly discourse
and industry practice regarding optimal API design workflows.
a 10% increased requirement fulfillment using API Improvement Proposals (AIPs) and linter versus no protocols. Meanwhile, 73% of 33 surveyed API consumers preferred AIP-aligned designs for enhanced usability and comprehensibility. Complementing this, a
custom large language model called the API Architect received average expert ratings of just 5/10 for specification quality, revealing gaps versus manual design. The quantitative performance metrics combined with qualitative user feedback provide evidence from
multiple angles that strategically integrating industry best practices with maturing AI capabilities can meaningfully improve API design outcomes. This research offers empirical insights from developer and consumer perspectives to advance scholarly discourse
and industry practice regarding optimal API design workflows.
Research Areas
