This project involves extracting requirements from user stories, establishing the parent-child relations among requirements, determining the AND/OR refinement types, and understanding other relations among requirements (temporal, conflict, contribution)
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.
This project is written in Python 3, uses pipenv for package management and optionally uses Docker for production. So it is assumed that you have these requirements installed.
Also you should install optimathsat and copy it to the project directory with the name optimathsat.
Note that it has different distributions for different operating systems. So install the one for your operating system as optimathsat and one for the linux as optimathsat_linux if you are going to use Docker
- If you are going to use
Dockerto run the project you can use this command in the project directory and see it onlocalhost:5000:
docker run -p 5000:5000 -d goal-model-generator
- If you are going to run the project locally you can benefit from
pipenv. In the project directory run these commands:
pipenv install
pipenv shell
flask run
If you want to deploy the app for yourself you can use docker-machine as I did to run the container on an AWS EC2 instance. You can of course use a better tier but smaller (free) tier lacks the memory so ...
docker-machine create --driver amazonec2 --amazonec2-instance-type "t2.small" --amazonec2-open-port 5000 goal-model-generator
This project is licensed under the MIT License - see the LICENSE file for details
We would like to express our very great appreciation to Dr. Aydemir for her valuable and constructive suggestions during the planning and development of this research work. Her willingness to give her time so generously has been very much appreciated.
You can find more example user stories included in the repo. Thanks to Dalpiaz, Fabiano (2018), “Requirements data sets (user stories)”, Mendeley Data, v1