White-Box Performance-Influence Models: A Profiling and Learning Approach

Many modern software systems are highly configurable, allowing the user to tune them for performance and more. Current performance modeling approaches aim at finding performance-optimal configurations by building performance models in a black-box manner. While these models provide accurate estimates...

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Vydáno v:Proceedings / International Conference on Software Engineering s. 1059 - 1071
Hlavní autoři: Weber, Max, Apel, Sven, Siegmund, Norbert
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.05.2021
Témata:
configuration management
Debugging
Java
Predictive models
Software engineering
Software measurement
software product lines
Software systems
software variability
Testing
ISBN:1665402962, 9781665402965
ISSN:1558-1225
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Shrnutí:Many modern software systems are highly configurable, allowing the user to tune them for performance and more. Current performance modeling approaches aim at finding performance-optimal configurations by building performance models in a black-box manner. While these models provide accurate estimates, they cannot pinpoint causes of observed performance behavior to specific code regions. This does not only hinder system understanding, but it also complicates tracing the influence of configuration options to individual methods. We propose a white-box approach that models configuration-dependent performance behavior at the method level. This allows us to predict the influence of configuration decisions on individual methods, supporting system understanding and performance debugging. The approach consists of two steps: First, we use a coarse-grained profiler and learn performance-influence models for all methods, potentially identifying some methods that are highly configuration-and performance-sensitive, causing inaccurate predictions. Second, we re-measure these methods with a fine-grained profiler and learn more accurate models, at higher cost, though. By means of 9 real-world Java software systems, we demonstrate that our approach can efficiently identify configuration-relevant methods and learn accurate performance-influence models.
ISBN:1665402962
9781665402965
ISSN:1558-1225
DOI:10.1109/ICSE43902.2021.00099