  Modern software systems are usually highly configurable, providing users with
customized functionality through various configuration options. Understanding
how system performance varies with different option combinations is important
to determine optimal configurations that meet specific requirements. Due to the
complex interactions among multiple options and the high cost of performance
measurement under a huge configuration space, it is challenging to study how
different configurations influence the system performance. To address these
challenges, we propose HINNPerf, a novel hierarchical interaction neural
network for performance prediction of configurable systems. HINNPerf employs
the embedding method and hierarchic network blocks to model the complicated
interplay between configuration options, which improves the prediction accuracy
of the method. Besides, we devise a hierarchical regularization strategy to
enhance the model robustness. Empirical results on 10 real-world configurable
systems show that our method statistically significantly outperforms
state-of-the-art approaches by achieving average 22.67% improvement in
prediction accuracy. In addition, combined with the Integrated Gradients
method, the designed hierarchical architecture provides some insights about the
interaction complexity and the significance of configuration options, which
might help users and developers better understand how the configurable system
works and efficiently identify significant options affecting the performance.

Software Engineering;Artificial Intelligence