Acapulco: An extensible tool for identifying optimal and consistent feature model configurations
Paper i proceeding, 2022

Configuring feature-oriented variability-rich systems is complex because of the large number of features and, potentially, the lack of visibility of the implications on quality attributes when selecting certain features. We present Acapulco as an alternative to the existing tools for automating the configuration process with a focus on mono- and multi-criteria optimization. The soundness of the tool has been proven in a previous publication comparing it to SATIBEA and MODAGAME. The main advantage was obtained through consistency-preserving configuration operators (CPCOs) that guarantee the validity of the configurations during the IBEA genetic algorithm evolution process. We present a new version of Acapulco built on top of FeatureIDE, extensible through the easy integration of objective functions, providing pre-defined reusable objectives, and being able to handle complex feature model constraints

Multiobjective optimization

variability management

software product lines

genetic algorithms

Software design

Författare

Jabier Martinez

Basque Research and Technology Alliance (BRTA)

Daniel Strüber

Radboud Universiteit

Göteborgs universitet

Software Engineering 2

Jose Miguel Horcas

Universidad De Malaga

Alexandru Burdusel

King's College London

Steffen Zschaler

King's College London

26th ACM International Systems and Software Product Line Conference

Vol. B 50-53
9781450392068 (ISBN)

26th ACM International Systems and Software Product Line Conference, ASPLC 2022
Graz, Austria,

Ämneskategorier (SSIF 2025)

Programvaruteknik

DOI

10.1145/3503229.3547067

Mer information

Senast uppdaterat

2025-06-27