JMH is the standard framework for developing and running Java microbenchmarks-lightweight performance tests used to evaluate the execution time of small Java code segments. A key challenge in designing JMH microbenchmarks is determining the appropriate number of warm-up iterations- repeated executions needed to bring microbenchmarks to a performance steady state. Too few warm-up iterations can compromise result quality, as performance measurements may not accurately reflect steady-state behavior. Conversely, too many warm-up iterations can unnecessarily increase testing time. Here, we present AMBER, an AI-enabled extension of JMH, which leverages Time Series Classification algorithms to predict the beginning of the steady-state phase at run-time and dynamically halt warm-up iterations accordingly. Empirical results show the potential of Amber in enhancing the cost-effectiveness of Java microbenchmarks. A demo video of Amber is available at https://www.youtube.com/watch?v=7zOngDQ1z_k.

AMBER: AI-Enabled Java Microbenchmark Harness

Trovato A.;Di Nucci D.
2025

Abstract

JMH is the standard framework for developing and running Java microbenchmarks-lightweight performance tests used to evaluate the execution time of small Java code segments. A key challenge in designing JMH microbenchmarks is determining the appropriate number of warm-up iterations- repeated executions needed to bring microbenchmarks to a performance steady state. Too few warm-up iterations can compromise result quality, as performance measurements may not accurately reflect steady-state behavior. Conversely, too many warm-up iterations can unnecessarily increase testing time. Here, we present AMBER, an AI-enabled extension of JMH, which leverages Time Series Classification algorithms to predict the beginning of the steady-state phase at run-time and dynamically halt warm-up iterations accordingly. Empirical results show the potential of Amber in enhancing the cost-effectiveness of Java microbenchmarks. A demo video of Amber is available at https://www.youtube.com/watch?v=7zOngDQ1z_k.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4912377
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