Backlog Modelling reference implementation

• Authors:
  • Sébastien Mosser
  • Corinne Pulgar
  • Vladimir Reinharz
• Contributors:
  • Benjamin Benni (Instant systems)
  • Guilhem Molines (IBM)

How to setup the environmenent?

On your local computer, we rely on Python 3.9 and pipenv to install dependencies

 pipenv install

The code uses numpy, a scientific computing library that could be complicated to setup locally. Thus, we provide a Docker version of the code that contains all the dependencies:

TODO

How to run the scenarios locally?

We assume here an up and running environment. The Makefile provide an easy way to start the scenarios using the right environment. For example, to run the first scenario:

make scenario_1

By convention, each scenario produces a PDF file as output, stored in the output directory

How to run the scenarios (with docker)?

As the scenarios can consume a lot of memory (the twoi last ones), be sure that you have allocated up to 4Gb of RAM to containers in your local Docker settings.

To run a scenario, use the docker_run.sh script, indicating which scenario you want to execute and which directory to be used as output.

How to access to the datasets?

We release as part of this artefact the following datasets:

• dataset/raw: the original files published by Dalpiaz in 2018;
• dataset/gilson: the result of the analysis of these stories by the the team of the University of Canterburry (NZ);
• dataset/cases: a JSON format of each stories processed by Visual Narrator and the approach form gilson et al.

If you want to run Visual Narrator on top of these file, we provide a docker image and a shell script to make it easier (in dataset/raw/visual_narrator.sh).

How to acces to the result without recomputing?

The resulting figures for each dataset are available in the results directory.

How to access to the code of each scenairo?

The Python code used to support each scenario is available in the scenarios directory. When a scenario requires sopme additional data (e.g., ground truth, or exat information), these data are stored in the scenarios/data directory.

How to access to the metamodel?

The metamodel and graph-based implementation of the approach is stored in the backlog directory.