  Despite the huge spread and economical importance of configurable software
systems, there is unsatisfactory support in utilizing the full potential of
these systems with respect to finding performance-optimal configurations. Prior
work on predicting the performance of software configurations suffered from
either (a) requiring far too many sample configurations or (b) large variances
in their predictions. Both these problems can be avoided using the WHAT
spectral learner. WHAT's innovation is the use of the spectrum (eigenvalues) of
the distance matrix between the configurations of a configurable software
system, to perform dimensionality reduction. Within that reduced configuration
space, many closely associated configurations can be studied by executing only
a few sample configurations. For the subject systems studied here, a few dozen
samples yield accurate and stable predictors - less than 10% prediction error,
with a standard deviation of less than 2%. When compared to the state of the
art, WHAT (a) requires 2 to 10 times fewer samples to achieve similar
prediction accuracies, and (b) its predictions are more stable (i.e., have
lower standard deviation). Furthermore, we demonstrate that predictive models
generated by WHAT can be used by optimizers to discover system configurations
that closely approach the optimal performance.
