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- Helali Moghadam, Mahshid, et al.
(author)
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Intelligent Load Testing: Self-adaptive Reinforcement Learning-driven Load Runner
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Other publication (other academic/artistic)abstract
- Load testing with the aim of generating an effective workload to identify performance issues is a time-consuming and complex challenge, particularly for evolving software systems. Current automated approaches mainly rely on analyzing system models and source code, or modeling of the real system usage. However, that information might not be available all the time or obtaining it might require considerable effort. On the other hand, if the optimal policy for generating the proper test workload resulting in meeting the objectives of the testing can be learned by the testing system, testing would be possible without access to system models or source code. We propose a self-adaptive reinforcement learning-driven load testing agent that learns the optimal policy for test workload generation. The agent can reuse the learned policy in subsequent testing activities such as meeting different types of testing targets. It generates an efficient test workload resulting in meeting the objective of the testing adaptively without access to system models or source code. Our experimental evaluation shows that the proposed self-adaptive intelligent load testing can reach the testing objective with lower cost in terms of the workload size, i.e. the number of generated users, compared to a typical load testing process, and results in productivity benefits in terms of higher efficiency.
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