Predicting performance via automated feature-interaction detection
Predicting performance via automated feature-interaction detection
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DOI：
10.1109/icse.2012.6227196
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发表时间：
2012-06
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影响因子：
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通讯作者：
Norbert Siegmund;Sergiy S. Kolesnikov;Christian Kästner;S. Apel;D. Batory;Marko Rosenmüller;G. Saake
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文献类型：
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作者：
Norbert Siegmund;Sergiy S. Kolesnikov;Christian Kästner;S. Apel;D. Batory;Marko Rosenmüller;G. Saake
可自定义的程序和程序系列提供了可选择的功能，以允许用户对应用程序方案量身定制程序。事先了解哪些特征选择产生最佳性能是困难的，因为对所有可能的特征组合的直接测量是不可行的。我们的工作旨在根据选定功能来预测程序性能。但是，当功能相互作用时，准确的预测具有挑战性。当特定特征组合对性能产生意外影响时，就会发生交互。我们提出了一种自动检测与性能相关的特征相互作用以提高预测准确性的方法。为此，我们提出了三种启发式方法，以减少检测相互作用所需的测量数量。我们的评估包括使用不同的配置技术（例如，配置文件和预处理标志）的六个现实世界中的案例研究（例如，数据库，编码库和Web服务器）。结果显示平均预测准确性为95％。
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%.