Mastering Uncertainty in Performance Estimations of Configurable Software Systems

Understanding the influence of configuration options on performance is key for finding optimal system configurations, system understanding, and performance debugging. In prior research, a number of performance-influence modeling approaches have been proposed, which model a configuration option'...

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Veröffentlicht in:	2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE) S. 684 - 696
Hauptverfasser:	Dorn, Johannes, Apel, Sven, Siegmund, Norbert
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	ACM 01.09.2020
Schlagworte:	Computing methodologies → Uncertainty quantification Configurable Software Systems Performance-Influence Modeling Predictive models Probabilistic logic Probabilistic Programming Programming Software and its engineering → Software performance Software engineering Software reliability Software systems Uncertainty
ISSN:	2643-1572
Online-Zugang:	Volltext
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