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ICSE
2012
IEEE-ACM

Predicting performance via automated feature-interaction detection

6 years 7 months ago
Predicting performance via automated feature-interaction detection
Abstract—Customizable programs and program families provide user-selectable features to allow users 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. However, when features interact, accurate predictions are challenging. An interaction occurs when a particular feature combination has an unexpected influence on performance. We present a method that automatically detects performance-relevant 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, encoding libraries, and web servers) using different configuration techniques (e.g., configuration file...
Norbert Siegmund, Sergiy S. Kolesnikov, Christian
Added 29 Sep 2012
Updated 29 Sep 2012
Type Journal
Year 2012
Where ICSE
Authors Norbert Siegmund, Sergiy S. Kolesnikov, Christian Kästner, Sven Apel, Don S. Batory, Marko Rosenmüller, Gunter Saake
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