Quality-aware Analysis and Optimisation of Virtual Network Function

SPLC'22 Quality-aware Analysis and Optimisation of Virtual Network Functions   DATA: Quality-aware Analysis and Optimisation of Virtual Network Functions   This repository contains the models, operations and results empirically used in Quality-aware Analysis and Optimisation of Virtual Network Functions at SPLC 2022. Due to copyright issues, it does not contain the tools (i.e., automated reasoners), although their official sites are provided.   It is licensed under the MIT license.     ## SPLC'22 Models, Categorical Operations and Datasets   This data-set contains:   1. The 5 SPL categories in CQL alongside the 11 tested operations. 2. The 5 SPL Clafer models. 3. The 5 SPL XMLs (for the AAFM Python Framework). 4. The 5 SPL XMLs (for SATIBEA). 5. The previous models are enriched with quality attributes measurements at feature and configuration levels. 6. A Microsoft Excel file with the scalability results obtained.     ## Automated Reasoners   - CQL IDE: https://github.com/CategoricalData/CQL - Clafermoo: http://t3-necsis.cs.uwaterloo.ca:8092/ - AAFM Python Framework: https://pypi.org/project/famapy/ - SATIBEA: https://github.com/jmguo/SMTIBEA     ## Requirements   The data-set has been generated using Java JDK 18.0.2 for CQL IDE, Clafermoo, and SATIBEA, and Python 3.9.13 x86_64 for AAFM Python Framework.   ## Authors   1. Daniel-Jesus Munoz: ITIS Software, CAOSD, Dpt. LCC, Universidad de Málaga, Andalucía Tech, Spain 2. Mónica Pinto: ITIS Software, CAOSD, Dpt. LCC, Universidad de Málaga, Andalucía Tech, Spain 3. Lidia Fuentes: ITIS Software, CAOSD, Dpt. LCC, Universidad de Málaga, Andalucía Tech, Spain