Predicting performance via automated feature-interaction detection

  • Authors:

  • Norbert Siegmund;Sergiy S. Kolesnikov;Christian Kästner;Sven Apel;Don Batory;Marko Rosenmüller;Gunter Saake

  • Affiliations:

  • University of Magdeburg, Germany;University of Passau, Germany;Philipps University of Marburg, Germany;University of Passau, Germany;University of Texas at Austin, USA;University of Magdeburg, Germany;University of Magdeburg, Germany

  • Venue:
  • Proceedings of the 34th International Conference on Software Engineering
  • Year:
  • 2012

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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 files and preprocessor flags). Results show an average prediction accuracy of 95%.