Dynamically reconfiguring software microbenchmarks: Reducing execution time without sacrificing result quality
Paper i proceeding, 2020

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.

Software benchmarking

Configuration

Performance testing

JMH

Författare

Christoph Laaber

Universität Zürich

Stefan Würsten

Universität Zürich

Harald C. Gall

Universität Zürich

Philipp Leitner

Chalmers, Data- och informationsteknik, Software Engineering, Software Engineering for Cyber Physical Systems

ESEC/FSE 2020 - Proceedings of the 28th ACM Joint Meeting European Software Engineering Conference and Symposium on the Foundations of Software Engineering

989-1001

28th ACM Joint Meeting European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE 2020
Virtual, Online, USA,

Utvecklarfokuserad prestandaförbättring för programvaruingenjörer

Vetenskapsrådet (VR), 2019-01-01 -- 2023-12-31.

Ämneskategorier

Programvaruteknik

Datavetenskap (datalogi)

Datorsystem

DOI

10.1145/3368089.3409683

Mer information

Senast uppdaterat

2020-12-18