Java Microbenchmark Harness ( JMH ) is the de facto standard framework for developing Java microbenchmarks—used to assess the performance of small code segments. A central challenge in microbenchmark design is determining the number of warm-up iterations required to reach steady-state execution: too few lead to inaccurate results, while too many introduce unnecessary overhead. This paper extends our previous contribution by providing a more detailed description of AMBER, an AI-enabled JMH extension that utilizes Time Series Classification to detect steady-state behavior at run-time and dynamically terminate warm-up iterations.
Trovato et al. (Wed,) studied this question.
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