A Combined Approach to Performance Regression Testing Resource Usage Reduction
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3A10507953" target="_blank" >RIV/00216208:11320/25:10507953 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1145/3727582.3728690" target="_blank" >https://doi.org/10.1145/3727582.3728690</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1145/3727582.3728690" target="_blank" >10.1145/3727582.3728690</a>
Alternative languages
Result language
angličtina
Original language name
A Combined Approach to Performance Regression Testing Resource Usage Reduction
Original language description
Performance regression testing is often seen as a natural part of the continuous integration pipeline. The underpinning layers, such as just-in-time compilation, memory mapping, and operating system characteristics, often influence performance measurement samples. To reduce such non-deterministic factors, the usual practice includes restarting the measured workload, performing warmups, and controlling environmental variability. These need to be parameterized, among others, by run count, warm-up iterations, and iteration count. Importantly, performance testing that detects performance regressions of any scale is computationally expensive due to the need to collect samples that can detect performance changes with statistical significance. To reduce the costs of performance testing, different methods for code analysis and experiment parameterization can be used. In this work, we address the challenge of identifying the optimal parameters for performance testing. Especially in environments that use just-in-time compilation, determining the required run count is non-trivial. The run count needed depends on the workload and non-deterministic factors. To address these challenges, we have developed an approach that combines several methods for parameter selection in performance testing automation. We created a simulation where these methods work together interactively, providing a dynamic environment to evaluate their effectiveness. We evaluated three controller methods on a public dataset from the GraalVM compiler. Based on our evaluation, find that the Peass method is most efficient if the change effect size of the training set mirrors the change effect size of the test set, and that the Mutations method has constant accuracy regardless of the training set data.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
PROCEEDINGS OF THE 2025 21ST INTERNATIONAL CONFERENCE ON PREDICTIVE MODELS AND DATA ANALYTICS IN SOFTWARE ENGINEERING, PROMISE 2025
ISBN
979-8-4007-1594-5
ISSN
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e-ISSN
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Number of pages
10
Pages from-to
75-84
Publisher name
ASSOC COMPUTING MACHINERY
Place of publication
NEW YORK
Event location
Trondheim
Event date
Jun 26, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001543698200009