Lightweight, Semi-Automatic Variability Extraction: A Case Study on Scientific Computing (Supplementary Website)
Authors: Alexander Grebhahn, Christian Kaltenecker, Christian Engwer, Norbert Siegmund, Sven Apel
Journal: Empirical Software Engineering
Corresponding Author: Alexander Grebhahn (adesso SE, Alexander.Grebhahn@adesso.de)
This repository contains the supplemental material for the paper 'Lightweight, Semi-Automatic Variability Extraction: A Case Study on Scientific Computing'. In particular, the supplemental material consists of the following:
- Implementation of our approach (VORM)
- Results of the evaluation
We supply a Dockerfile to deploy and execute our approach in a docker container.
First, make sure to clone the repository.
To set up the Docker container, make also sure that you have properly installed Docker (https://docs.docker.com/get-started/) and the Docker daemon is running.
Afterwards, the image can be built by invoking the following command inside the repository.
sudo docker build -t variabilityextraction ./
After building the image, an interactive session in a docker container can be started by:
sudo docker run -ti variabilityextraction
When this image is built, VORM is executed in a last RUN step inside the Dockerfile.
This produces the results that are presented inside the paper.
The results for the linearsolver are included in the directory /application/VariabilityExtraction-SupplementaryWebsite/SPLConqueror_Dune/Results_linearsolver/results.txt and the results for the ellipticproblem are included in the directory /application/VariabilityExtraction-SupplementaryWebsite/SPLConqueror_Dune/Results_ellipticproblem/results.txt.
The results of the linearsolver are included in the file linearsolver_alternatives.txt and the results of the ellipticproblem are included in the file ellipticproblem_alternatives.txt.
The categorization of the results (e.g., true false positive) is included in linearsolver_alternatives_categorization.txt and ellipticproblem_alternatives.txt, respectively.