Predicting Performance of Software Configurations: There is no Silver Bullet
This addresses the challenge for software users in efficiently finding performance-optimal configurations, but it is incremental as it builds on existing supervised learning methods without introducing new paradigms.
The study tackled the problem of predicting optimal software configurations for performance by comparing 6 machine-learning techniques and 18 sampling strategies across 6 software systems, finding that prediction accuracy varies strongly with the technique, strategy, and system, with no universally optimal combination.
Many software systems offer configuration options to tailor their functionality and non-functional properties (e.g., performance). Often, users are interested in the (performance-)optimal configuration, but struggle to find it, due to missing information on influences of individual configuration options and their interactions. In the past, various supervised machine-learning techniques have been used to predict the performance of all configurations and to identify the optimal one. In the literature, there is a large number of machine-learning techniques and sampling strategies to select from. It is unclear, though, to what extent they affect prediction accuracy. We have conducted a comparative study regarding the mean prediction accuracy when predicting the performance of all configurations considering 6 machine-learning techniques, 18 sampling strategies, and 6 subject software systems. We found that both the learning technique and the sampling strategy have a strong influence on prediction accuracy. We further observed that some learning techniques (e.g., random forests) outperform other learning techniques (e.g., k-nearest neighbor) in most cases. Moreover, as the prediction accuracy strongly depends on the subject system, there is no combination of a learning technique and sampling strategy that is optimal in all cases, considering the tradeoff between accuracy and measurement overhead, which is in line with the famous no-free-lunch theorem.