MANILA Framework

This repository cointains the source code of the MANILA framework described in the papers Democratizing Quality-Based Machine Learning Development through Extended Feature Models and A Framework to Democratize the Quality-Based Machine Learning Development Through Extended Feature Models: Strengths and Limitations.

Citation Request

Please cite our work if you use it in your research:

G. d’Aloisio, A. Di Marco, e G. Stilo, «Democratizing Quality-Based Machine Learning Development through Extended Feature Models», in Fundamental Approaches to Software Engineering, L. Lambers e S. Uchitel, A c. di, in Lecture Notes in Computer Science. Cham: Springer Nature Switzerland, 2023, pp. 88–110. doi: 10.1007/978-3-031-30826-0_5.

@inproceedings{daloisio_democratizing_2023,
	address = {Cham},
	series = {Lecture {Notes} in {Computer} {Science}},
	title = {Democratizing {Quality}-{Based} {Machine} {Learning} {Development} through {Extended} {Feature} {Models}},
	copyright = {All rights reserved},
	isbn = {978-3-031-30826-0},
	doi = {10.1007/978-3-031-30826-0_5},
	language = {en},
	booktitle = {Fundamental {Approaches} to {Software} {Engineering}},
	publisher = {Springer Nature Switzerland},
	author = {d’Aloisio, Giordano and Di Marco, Antinisca and Stilo, Giovanni},
	editor = {Lambers, Leen and Uchitel, Sebastián},
	year = {2023},
	pages = {88--110},
}

Project Structure

This project is structured in the following way:

• generator: this folder contains all the files needed to generate an experiment workflow
  • templates: this folder contains all the Jinja templates used to generate the needed files
  • generator.py: main file to generate the experiment workflow
• manila-fm: this folder contains the implemented Extended Feature Model and the relative configuration specifications
  • configs: directory in which the configuration files are stored
  • feature-model.png: full picture of the Extended Feature Model
  • model.xml: specification of the Extended Feature Model
• imgs: this directory contains the images used in this README
• replication_package: this directory contains the replication package of the experiments conducted in section 5.2 of the paper. Refer to the README in the directory for more information.

MANILA Setup

Please follow these steps to setup the MANILA framework:

Environment setup

1. Install FeatureIDE following the instructions reported at the following link
2. Install Miniconda following the instructions reported at the following link
3. Create a new conda environment and install the Jinja package using these commands:

$ conda create -n manila_env python=3.9
$ conda activate manila_env
$ pip install Jinja2

Importing the Extended Feature Model in FeatureIDE

1. Start Eclipse and select the root folder as workspace:

2. From the opened window click of File -> Import -> Existing Project into Workspace, select the root folder as the root directory and manila-fm should appear among the projects:

3. Click on Finish and the project should appear on the left panel:

4. Click on the model.xml file to open the Extended Feature Model:

5. Open the config.xml file to see an example configuration of an experiment:

License

This work is distributed under AGPL v3 License