H2020 DAEMON: Network intelligence for aDAptive and sElf-Learning MObile Networks

Part of EU Open Research Repository
Published February 24, 2022 | Version v1
Dataset Open

Smart Analyser of Variability Requirements of Unknown Spaces (SAVRUS) Dataset of a study with 5 real-world large numerical variability models.

Creators

  • 1. ITIS Software, CAOSD, Universidad de Málaga

Description

The publications and research associated to cite is in:

https://doi.org/10.1016/j.knosys.2023.110558

In that research we detail the Smart Analyser of Variability Requirements of Unknown Spaces (SAVRUS) approach, and provide a web-tool prototype in https://hadas.caosd.lcc.uma.es/savrus

In the study, we model 5 different real-world software product lines to then analysed them with SAVRUS:

Detailed real-world variability models ordered by their search space size, of which GEC QA is incompletely measured NVM Description #Booleans #Numericals Space QA #Measurements 

Dune1

 

Multi-grid solver

 

11

 

3

 

2,304

 

Complex..

 

2,304

 

HSMGP1

 

Stencil-grid solver

 

14

 

3

 

3,456

 

..equation..

 

3,456

 

HiPAcc1

 

Image processing framework

 

33

 

2

 

13,485

 

..solving..

 

13,485

 

Trimesh2

 

Triangle mesh library

 

13

 

4

 

239,360

 

..time

 

239,360

 

GEC

 

Generic edge computing

 

552

 

2

 

~5.3*108

 

Energy Consumption

 

132500

 

The dataset zip file contains:

  • 5 numerical variability models in Clafer format (.txt) for each software product line.
  • 5 CSV files with the respective quality attribute measurements
  • An .xlsx file containing SAVRUS scalability results divided in different tabs.

References:

[1] N. Siegmund, A. Grebhahn, S. Apel, C. Kastner, Performance-influence models for highly configurable systems, in: Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering, ESEC/FSE 2015, Association for Computing Machinery, New York, NY, USA, 2015, p.284–294. doi:10.1145/2786805.2786845.

[2] M. Bauer, A comparison of six constraint solvers for variability analysis, Tech. rep., University of Passau (2019).

Notes

This work is supported by the European Union's H2020 research and innovation programme under grant agreement DAEMON 101017109, by the projects co-financed by FEDER funds LEIA UMA18-FEDERJA-15, MEDEA RTI2018-099213-B-I00 and Rhea P18-FR-1081 and the PRE2019-087496 grant from the Ministerio de Ciencia e Innovación.

Files

SuplementaryFSCG.zip

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Additional details

Related works

Is previous version of
Conference paper: 10.1007/978-3-031-08129-3_4 (DOI)

Funding

European Commission
DAEMON - Network intelligence for aDAptive and sElf-Learning MObile Networks 101017109

References

  • N. Siegmund, A. Grebhahn, S. Apel, C. Kastner, Performance-influence models for highly configurable systems, in: Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering, ESEC/FSE 2015, Association for Computing Machinery, New York, NY, USA, 2015, p.284–294. doi:10.1145/2786805.2786845.
  • M. Bauer, A comparison of six constraint solvers for variability analysis, Tech. rep., University of Passau (2019).