Skip to content
Material about tutorial "Machine Learning and Software Configurable Systems: A Gentle Introduction"
Jupyter Notebook
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Type Name Latest commit message Commit time
Failed to load latest commit information.

Tutorial "Machine Learning and Software Configurable Systems: A Gentle Introduction"


Slides: see slides folder:

  • MLTutorialSPLC19-VaryLaTeX.pdf
  • MLTutorialSPLC19-SLR.pdf
  • MLTutorialSPLC19-VaryLaTeXExercice.pdf
  • MLTutorialSPLC19-x264Exercice.pdf
  • MLTutorialSPLC19-WrapUp.pdf


  • Welcome and general motivation: Why machine learning is relevant for engineering software configurable systems?
  • The VaryLaTeX case (demonstration)
  • see MLTutorialSPLC19-VaryLaTeX.pdf
  • Overview: An overview of works in the field, see MLTutorialSPLC19-SLR.pdf
    • based on a systematic literature survey
    • we describe the different applications (pure prediction, optimization, specialization, understanding, etc.)
    • we review subject systems and application domains
    • we describe numerous sampling strategies
    • we detail how configurations are measured
    • we report on learning algorithms used and their assessment
  • Setup instructions (1020 => 1030)
  • Practical session 1: learning-based specialization with VaryLaTeX case (1100 => 1145, see MLTutorialSPLC19-VaryLaTeXExercie.pdf)
  • information about VaryLaTeX:
  • dataset:
  • decision tree algorithm and a focus on interpretability
  • we use Python and Jupyter notebooks
  • exercices:
    • change the training set size and analyze the effect on accuracy and rules
    • change some hyperparameters
    • change the algorithm (using random forest)
  • Practical session 2: performance prediction with x264 case (1145 => 1215, see MLTutorialSPLC19-x264Exercice.pdf)
  • dataset from the literature
  • we use Python and Jupyter notebooks
  • Summary and open research directions (1215 => 1230, see MLTutorialSPLC19-WrapUp.pdf)
    • wrap-up
    • open issues


requirements: Jupyter, Python 3 with scikit-learn, pandas, and numpy play with notebooks in latex (for VaryLaTeX exercice) and x264 (for x264 exercice)

pip install pandas
pip install numpy
pip install scikit-learn
pip install jupyter
pip install graphviz
You can’t perform that action at this time.