logs directory contains measurement data and learned performance-influence models for each subject system: _sosym.a - SPLConqueror automation files. These are used to learn performance-influence models using SPLConqueror. Each directory of subject system contains: all_measurements.xml - performance measurements in an SPLConqueror format feature_model.xml - variability model in an SPLConqueror format feature_model.guidsl - variability model in a GUIDSL format feature_model.png - a feature diagram representation of the variability model sosym_stdout - log file with learned performance-influence models notebooks/results_analysis.ipynb is a jupyter notebook in Python 3 that analyses the data from logs directory and generates plots. noise directory contains scripts for generating plots describing influence of measurement errors on the performance-influence model accuracy (see supplementary website http://fosd.de/tradoffs/). The corresponding measurements data is in logs/sanity_check (structured the same way as logs directory).