Variability-Aware Performance Modeling and Prediction

Many software systems provide configuration options for users to tailor their functional behavior as well as non-functional properties (e.g., performance, cost, and energy consumption). Configuration options relevant to users are often called features. Each variant derived from a configurable software system can be represented as a selection of features, called a configuration.

Performance (e.g., response time or throughput) is one of the most important non-functional properties, because it directly affects user perception and cost. To find an optimal configuration to meet a specific performance goal, it is crucial for developers and IT administrators to understand the correlation between feature selections and performance.

We investigate a practical approach that mines such a correlation from a sample of measured configurations, specifies the correlation as an explicit performance prediction model, and then uses the model to predict the performance of other unmeasured configurations.

More details, implementation code, and experimental data are available on an open-source project website: http://cpm.googlecode.com.

Team Members

News

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Publications

2015
Sarkar, A., J. Guo, N. Siegmund, S. Apel, and K. Czarnecki, "Cost-Efficient Sampling for Performance Prediction of Configurable Systems", 30th IEEE/ACM International Conference on Automated Software Engineering (ASE), Lincoln, Nebraska, USA, IEEE, 11/2015. [pdf]
Zhang, Y., J. Guo, E. Blais, and K. Czarnecki, "Performance Prediction of Configurable Software Systems by Fourier Learning", 30th IEEE/ACM International Conference on Automated Software Engineering (ASE), Lincoln, Nebraska, USA, 11/2015. [pdf][pdf]
2013
Guo, J., K. Czarnecki, S. Apel, N. Siegmund, and A. Wąsowski, "Variability-Aware Performance Prediction: A Statistical Learning Approach", 28th IEEE/ACM International Conference on Automated Software Engineering (ASE), Silicon Valley, California, USA, IEEE, 11/2013. [pdf][pdf]
Guo, J., K. Czarnecki, S. Apel, N. Siegmund, and A. Wąsowski, Why CART Works for Variability-Aware Performance Prediction? An Empirical Study on Performance Distributions, , Waterloo, Generative Software Development Laboratory, University of Waterloo, 04/2013. [pdf]
2012
Guo, J., K. Czarnecki, S. Apel, N. Siegmund, and A. Wąsowski, Variability-Aware Performance Modeling: A Statistical Learning Approach, , Waterloo, Generative Software Development Laboratory, University of Waterloo, 08/2012. [pdf]