Performance-Influence Models for Highly Configurable Systems

Almost every complex software system today is configurable. While configurability has many benefits, it challenges per-formance prediction, optimization, and debugging. Often, the influences of individual configuration options on per-formance are unknown. Worse, configuration options may interact, giving rise to a configuration space of possibly ex-ponential size. Addressing this challenge, we propose an approach that derives a performance-influence model for a given configurable system, describing all relevant influences of configuration options and their interactions. Our ap-proach combines machine-learning and sampling heuristics in a novel way. It improves over standard techniques in that it (1) represents influences of options and their interactions explicitly (which eases debugging), (2) smoothly integrates binary and numeric configuration options for the first time, (3) incorporates domain knowledge, if available (which eases learning and increases accuracy), (4) considers complex con-straints among options, and (5) systematically reduces the solution space to a tractable size. A series of experiments demonstrates the feasibility of our approach in terms of the accuracy of the models learned as well as the accuracy of the performance predictions one can make with them