SPLC'23 - Analysis Operations On The Run

Code repository for SPLC'23 - Analysis Operations On The Run: Beyond Static Feature Model Analysis
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About The Project

This project uses FLAMA for computing all the necesary operations in the paper "SPLC'23 - Analysis Operations On The Run: Beyond Static Feature Model Analysis"

Intended workflow explained:

• User deploys the API with Docker (see Instalation)
• User access the API, reads /api/v1/docs
• User can now start using the API

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Built With

• Docker
• Flask
• FLAMA
• Flasgger

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Getting Started

To get a local copy up and running follow these simple example steps.

First, you will need to install and start Docker.

Instalation

1. Clone the repository

2. If you are running Windows, run

$ cd flamapy-api
$ ./start-server.cmd

3. If you are running Linux or MacOS, run

$ cd flamapy-api
$ ./start-server.sh

This script will build, install and deploy the API in http://localhost:5000, you can access all the endpoints through an application like Postman

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API Documentation

All the documentation is registered with Swagger UI and OAS 3.0. It is accesible through /api/docs.

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Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement".

Don't forget to give the project a star! Thanks again!

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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