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Context-tailored Workload Model Generation for Continuous Representative Load Testing

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. We evaluated our approach with the workload of a student information system. Our results show that incrementally learned workload models can be used for generating tailored load tests. The description language is able to express the relevant contexts, which, in turn, improve the representativeness of the load tests. We have also found that the existing workload characterization concepts and forecasting tools used are limited in regard to strong workload fluctuations, which needs to be tackled in future work.