Dynamically reconfiguring software microbenchmarks: reducing execution time without sacrificing result quality
Christoph Laaber, Stefan Würsten, Harald C. Gall, Philipp Leitner
Abstract
Executing software microbenchmarks, a form of small-scale performance tests predominantly used for libraries and frameworks, is a costly endeavor. Full benchmark suites take up to multiple hours or days to execute, rendering frequent checks, e.g., as part of continuous integration (CI), infeasible. However, altering benchmark configurations to reduce execution time without considering the impact on result quality can lead to benchmark results that are not representative of the software's true performance.
We propose the first technique to dynamically stop software microbenchmark executions when their results are sufficiently stable. Our approach implements three statistical stoppage criteria and is capable of reducing Java Microbenchmark Harness (JMH) suite execution times by 48.4% to 86.0%. At the same time it retains the same result quality for 78.8% to 87.6% of the benchmarks, compared to executing the suite for the default duration.
The proposed approach does not require developers to manually craft custom benchmark configurations; instead, it provides automated mechanisms for dynamic reconfiguration. Hence, making dynamic reconfiguration highly effective and efficient, potentially paving the way to inclusion of JMH microbenchmarks in CI.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 4453e4d4-d03f-47b3-8f83-b9e6d468add7Cited by top-tier papers4
- AI-driven Java Performance Testing: Balancing Result Quality with Testing TimeLuca Traini, Federico Di Menna, Vittorio CortellessaASE 2024 · 12 citations
- Performance Testing for Cloud Computing with Dependent Data BootstrappingSen He, Tianyi Liu, Palden Lama, Jaewoo Lee et al.ASE 2021 · 12 citations
- COFFE: A Code Efficiency Benchmark for Code GenerationYun Peng, Jun Wan, Yichen Li, Xiaoxue RenFSE 2025 · 8 citations
- Experimental Evaluation Methodology for the Era of No Steady PerformanceJaromír Antoch, Walter Binder, Lubomír Bulej, François Farquet et al.OOPSLA 2026
Builds on1
Related papers
- Profiling-Guided Bayesian Optimization of JVM ConfigurationsAbdelrahman Baz, Wing Lam, August ShiISSTA 2026
- Reducing Test Runtime by Transforming Test FixturesChengpeng Li, Abdelrahman Baz, August ShiASE 2024 · 1 citation
- LLM4JMH: Studying the Use of LLMs for Generating Java Performance MicrobenchmarksZongxiong Chen, Derui Zhu, Kundi Yao, Weiyi Shang et al.ICSE 2026
- JShrink: in-depth investigation into debloating modern Java applicationsBobby R. Bruce, Tianyi Zhang, Jaspreet Arora, Guoqing Harry Xu et al.FSE 2020 · 46 citations
- A cost-efficient approach to building in continuous integrationXianhao Jin, Francisco ServantICSE 2020 · 34 citations
