Is seeding a good strategy in multi-objective feature selection when feature models evolve?

DC FieldValueLanguage
dc.contributor.authorSaber, Takfarinas
dc.contributor.authorBrevet, David
dc.contributor.authorBotterweck, Goetz
dc.contributor.authorVentresque, Anthony
dc.date.accessioned2017-09-18T12:28:41Z
dc.date.copyright2017 Elsevieren
dc.date.issued2018-03
dc.identifier.citationInformation and Software Technologyen
dc.identifier.urihttp://hdl.handle.net/10197/8766
dc.description.abstractContext: When software architects or engineers are given a list of all the features and their interactions (i.e., a Feature Model or FM) together with stakeholders 'preferences' their task is to find a set of potential products to suggest the decision makers. Software Product Lines Engineering (SPLE) consists in optimising those large and highly constrained search spaces according to multiple objectives reflecting the preference of the different stakeholders. SPLE is known to be extremely skill- and labour-intensive and it has been a popular topic of research in the past years.Objective: This paper presents the first thorough description and evaluation of the related problem of evolving software product lines. While change and evolution of software systems is the common case in the industry, to the best of our knowledge this element has been overlooked in the literature. In particular, we evaluate whether seeding previous solutions to genetic algorithms (that work well on the general problem) would help them to find better/faster solutions.Method: We describe in this paper a benchmark of large scale evolving FMs, consisting of 5 popular FMs and their evolutions – synthetically generated following an experimental study of FM evolution. We then study the performance of a state-of-the-art algorithm for multi-objective FM selection (SATIBEA) when seeded with former solutions.Results: Our experiments show that we can improve both the execution time and the quality of SATIBEA by feeding it with previous configurations. In particular, SATIBEA with seeds proves to converge an order of magnitude faster than SATIBEA alone.Conclusion: We show in this paper that evolution of FMs is not a trivial task and that seeding previous solutions can be used as a first step in the optimisation - unless the difference between former and current FMs is high, where seeding has a limited impact.en
dc.language.isoenen
dc.publisherElsevieren
dc.rightsThis is the author’s version of a work that was accepted for publication in Information and Software Technology. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Information and Software Technology, 2017-08. DOI: 10.1016/j.infsof.2017.08.010en
dc.subjectSoftware product linesen
dc.subjectMulti-objectiveen
dc.subjectGenetic algorithmen
dc.subjectEvolutionen
dc.titleIs seeding a good strategy in multi-objective feature selection when feature models evolve?en
dc.typeJournal Articleen
dc.internal.authorcontactotheranthony.ventresque@ucd.ie
dc.statusPeer revieweden
dc.identifier.volume95
dc.identifier.doi10.1016/j.infsof.2017.08.010-
dc.neeo.contributorSaber|Takfarinas|aut|-
dc.neeo.contributorBrevet|David|aut|-
dc.neeo.contributorBotterweck|Goetz|aut|-
dc.neeo.contributorVentresque|Anthony|aut|-
dc.date.embargo2019-08-30
dc.description.othersponsorshipLeroen
dc.internal.rmsid798650665
dc.date.updated2017-08-31T12:09:48Z
item.grantfulltextembargo_20190830-
item.fulltextWith Fulltext-
Appears in Collections:Computer Science Research Collection
PEL Research Collection
Files in This Item:
Access to this item has been restricted by the copyright holder until:2019-08-30
File Description SizeFormat 
Information_and_Software_Technology_2017_manuscript.pdf986.1 kBAdobe PDFDownload    Request a copy
Show simple item record

SCOPUSTM   
Citations 50

2
Last Week
0
Last month
checked on Jul 18, 2019

Google ScholarTM

Check

Altmetric


This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.