Context-tailored Workload Model Generation for Continuous Representative Load Testing
Henning Schulz, Dušan Okanović, André van Hoorn, Petr Tůma · 2021
Load tests evaluate software quality attributes, such as performance and reliability, by e.g., emulating user behavior that is representative of the production workload. Existing approaches extract workload models from recorded user requests. However, a single workload model cannot reflect the complex and evolving workload of today's applications, or take into account workload-influencing contexts, such as special offers, incidents, or weather conditions. In this paper, we propose an integrated framework for generating load tests tailored to the context of interest, which a user can describe in a language we provide. The framework applies multivariate time series forecasting for extracting a context-tailored load test from an initial workload model, which is incrementally learned by clustering user sessions recorded in production and enriched with relevant context information.