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A hybrid approach to suggest software product line portfolios
Federal University of Piauí, BRA.
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för programvaruteknik.
Federal University of Piauí, BRA.
Federal University of Piauí, BRA.
Vise andre og tillknytning
2016 (engelsk)Inngår i: Applied Soft Computing, ISSN 1568-4946, E-ISSN 1872-9681, Vol. 49, s. 1243-1255Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Software product line (SPL) development is a new approach to software engineering which aims at the development of a whole range of products. However, as long as SPL can be useful, there are many challenges regarding the use of that approach. One of the main problems which hinders the adoption of software product line (SPL) is the complexity regarding product management. In that context, we can remark the scoping problem. One of the existent ways to deal with scoping is the product portfolio scoping (PPS). PPS aims to define the products that should be developed as well as their key features. In general, that approach is driven by marketing aspects, like cost of the product and customer satisfaction. Defining a product portfolio by using the many different available aspects is a NP-hard problem. This work presents an improved hybrid approach to solve the feature model selection problem, aiming at supporting product portfolio scoping. The proposal is based in a hybrid approach not dependent on any particular algorithm/technology. We have evaluated the usefulness and scalability of our approach using one real SPL (ArgoUML-SPL) and synthetic SPLs. As per the evaluation results, our approach is both useful from a practitioner's perspective and scalable. © 2016 Elsevier B.V.

sted, utgiver, år, opplag, sider
Elsevier, 2016. Vol. 49, s. 1243-1255
Emneord [en]
Feature model selection problem, Fuzzy inference systems, NSGA-II, Product portfolio scoping, Search based feature model selection, Search based software engineering, Software product lines, Computational complexity, Computer software, Customer satisfaction, Fuzzy inference, Software design, Software engineering, Feature modeling, Scoping, Search-based software engineering, Software Product Line, Feature extraction
HSV kategori
Identifikatorer
URN: urn:nbn:se:bth-13496DOI: 10.1016/j.asoc.2016.08.024ISI: 000392285600089Scopus ID: 2-s2.0-84994193828OAI: oai:DiVA.org:bth-13496DiVA, id: diva2:1049327
Tilgjengelig fra: 2016-11-24 Laget: 2016-11-23 Sist oppdatert: 2018-01-13bibliografisk kontrollert

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