Predicting Performance via Automated Feature-Interaction Detection

kaestner@cs.cmu.edu (N. Siegmund, S. Kolesnikov, C. Kästner, S. Apel, D. Batory, M. Rosenmüller, G. Saake)
Fri, 10 Jan 2025 13:48:21 GMT

Norbert Siegmund, Sergiy S. Kolesnikov, Christian Kästner, Sven Apel, Don Batory, Marko Rosenmüller, and Gunter Saake. Predicting Performance via Automated Feature-Interaction Detection. In Proceedings of the 34th International Conference on Software Engineering (ICSE), ISBN 978-1-4673-1067-3, pages 167--177, Los Alamitos, CA: IEEE Computer Society, 2012.

Customizable programs and program families provide user-selectable features to tailor a program to an application scenario. Knowing in advance which feature selection yields the best performance is difficult because a direct measurement of all possible feature combinations is infeasible. Our work aims at predicting program performance based on selected features. The challenge is predicting performance accurately when features interact. An interaction occurs when a feature combination has an unexpected influence on performance. We present a method that automatically detects performance feature interactions to improve prediction accuracy. To this end, we propose three heuristics to reduce the number of measurements required to detect interactions. Our evaluation consists of six real-world case studies from varying domains (e.g. databases, compression libraries, and web server) using different configuration techniques (e.g., configuration files and preprocessor flags). Results show, on average, a prediction accuracy of 95 %.