The code and the results of our paper: "Applying Deep Learning to Reduce Large Adaptation Spaces of Self-Adaptive Systems with Multiple Types of Goals"
The source code of the DLASeR framework can be found in the src folder.
In order to use the code the following two steps must be executed:
- Make sure you have all the dependencies installed. Execute the following command;
$ pip install -r requirements.txt - Import and use the code, see for example the Jupyter notebook
usage_example.ipynb.
The Jupyter notebooks containing the evaluation results are stored in the experiments/DLASeR/ folder.
This project is created by Jeroen Van Der Donckt, Federico Quin and Danny Weyns.
We are grateful to all other people whose work laid the foundations of this project.
We release our results and this code under MIT.
Even though MIT doesn't require it, we would like to ask if you could nevertheless cite our paper if it helped you!
@inproceedings{vanderdonckt2020applying,
title={Applying Deep Learning to Reduce Large Adaptation Spaces of Self-Adaptive Systems with Multiple Types of Goals},
author={Van Der Donckt, Jeroen and Weyns, Danny and Quin, Federico and Van Der Donckt, Jonas and Michiels, Sam},
booktitle={2020 IEEE/ACM 15th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)},
year={2020},
organization={IEEE}
}
The code and the results of our paper: "Deep Learning for Effective and Efficient Reduction of LargeAdaptation Spaces in Self-Adaptive Systems"
The source code of the DLASeR+ framework can be found in the src folder.
In order to use the code the following two steps must be executed:
- Make sure you have all the dependencies installed. Execute the following command;
$ pip install -r requirements.txt - Import and use the code, see for example the Jupyter notebook
usage_example.ipynb.
The Jupyter notebooks containing the evaluation results are stored in the experiments/DLASeR+/ folder.