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Hardware-based measurement

This is the data I collected and the analysis files used for a part of my bachelor's thesis using the method described by Junger et al.

Measurement files in the folder are structured as follows: Actions_SUT_Tesla.csv - logs start and stop of measurement cycles DJ-TESLA_DataCollector[xxx].csv - Several data on collected parameters, e.g. RAM, CPU, network traffic elektrische_leistungsmessung_Tesla.log - elctrical power used

/1

Data for decision tree measurements

/2

Data for logistic regression measurements

/3_NN

Data for multilayer perceptron measurements

/4_SVM

Data for support vector machine measurements

/Baseline_Tesla_WinAutomate_120s+60s_cooldown_new

Data for support vector machine measurements

/Baseline_Tesla_600+60

Data for support vector machine measurements

/analyse

[model/baseline].py - merges different files, structures and cleans data mean_to_bl.py - compares mean values of the measurement per model to baseline values methods.py - service class for the methods used across all analysis files ttest.ipynb - significance tests and effect size tests (ttest and cohen's d) shapiro-wilk.ipynb - tests for normality

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