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Distance-Based Sampling of Software Configuration Spaces
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Datum
2020
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Gesellschaft für Informatik e.V.
Zusammenfassung
Configurable software systems provide configuration options to adjust and optimize
their functional and non-functional properties. However, to obtain accurate performance predictions,
a representative sample set of configurations is required. Different sampling strategies have been
proposed, which come with different advantages and disadvantages. In our experiments, we found
that most sampling strategies do not achieve a good coverage of the configuration space with respect
to covering relevant performance values. That is, they miss important configurations with distinct
performance behavior. Based on this observation, we devise a new sampling strategy that is based on
a distance metric and a probability distribution to spread the configurations of the sample set across
the configuration space. To demonstrate the merits of distance-based sampling, we compare it to
state-of-the-art sampling strategies on 10 real-world configurable software systems. Our results show
that distance-based sampling leads to more accurate performance models for medium to large sample
sets.