From Ad Hoc to Repeatable: A Knowledge-Centric Framework for Introducing API Performance Testing

Abstract

API performance testing is often introduced informally and remains dependent on individual expertise, which limits reuse and  makes results difficult to repeat. This paper presents a lightweight framework and repository approach. It helps organizations  establish a repeatable API performance testing process and capture knowledge for reuse. Using a Design Science Research (DSR) approach, a Knowledge-Centric API Performance Testing Framework with six components was developed: Scope & SLOs,  Environment Setup, Data Preparation, Execution & Metrics, Analysis & Reporting, and Knowledge Capture & Sharing. A key distinguishing feature of the framework is its explicit inclusion of non-technical roles (e.g., Business Analysts and Product Owners), enabling them to contribute to performance testing through structured involvement in scope definition, governance, and knowledge capture. The framework was applied in a global IT organization during an initiative to validate API performance after a data source change. The case shows that a structured workflow and shared artefacts reduce repeated effort and support cross-functional collaboration. The paper contributes (1) a practical framework for organizations introducing API performance testing from scratch and (2) a knowledge management framing that treats performance testing outputs as reusable organizational assets.