|Sergiy Kolesnikov||Norbert Siegmund||Christian Kästner||Alexander Grebhahn||Sven Apel|
|University of Passau||Bauhaus-University Weimar||Carnegie Mellon University||University of Passau||University of Passau|
To ensure the replicability of our study, we provide the complete set of our experimental data and scripts. The data set includes:
For reproducing the results of the study it is easier to download the complete data set with all data in one direcotry structure. See the README file in the archive for a detailed description of the structure.
Steps for reproducing the results:
Download the complete data set: dataset.zip
Download plots: plt_noise_effect.pdf
For each algorithm variant there is one plot. A plot shows the influence of noise of different strength (x-axis) on the accuracy of the models (y-axis) of different systems. For each subject system there is a series of data points connected by a line. Each data point represents a performance-influence model of the system that was learned from the measurement data with corresponding noise (x-axis) and has the corresponding accuracy (y-axis).
We performed semi-structured interviews with three experts from the domain of high performance computing, who develop and work with performance-critical systems. During the interview we focused on the following issues:
After a short introduction about our performance-influence models, we presented three different performance-influence models of a system to the interviewee. To lighten understanding of the models, we select models for systems the interviewee work with and also presented them a variability model describing the variability we considered in our case study. The models differ in complexity, prediction accuracy, and computation time required to build the model. To improve readability of the models, we performed a preprocessing of the models, where we rounded the coefficients of the terms to three significant digits. During the interview, we asked them which of the models they prefer and why. When they mentioned the complexity, we asked which attributes they used to asses the complexity of the model. To get an impression about the complexity growth of a single term, we presented four terms describing the influence of an individual option and interactions up to a degree of four (four configuration options interacting with each other).
Download the material presented to the interviewees: Models.pdf