Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Mapping Extended Feature Models to Constraint Logic Programming over Finite Domains
Date
2010-09-17
Author
KARATAS, Ahmet Serkan
OGUZTUZUN, Halit
Doğru, Ali Hikmet
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
170
views
0
downloads
Cite This
As feature models for realistic product families may be quite complicated, automated analysis of feature models is desirable. Although several approaches reported in the literature addressed this issue, complex feature-attribute and attribute-attribute relationships in extended feature models were not handled effectively. In this article, we introduce a mapping from extended feature models to constraint logic programming over finite domains. This mapping is used to translate basic, cardinality-based, and extended feature models, which may include complex feature-feature, feature-attribute and attribute-attribute cross-tree relationships, into constraint logic programs. It thus enables use of off-the-shelf constraint solvers for the automated analysis of extended feature models involving such complex relationships. We also briefly discuss the ramifications of including feature-attribute relationships in operations of analysis. We believe that this proposal will be effective for further leveraging of constraint logic programming for automated analysis of feature models.
Subject Keywords
Constraint logic programming
,
Feature attribute
,
Extended feature model
,
Variability modeling
URI
https://hdl.handle.net/11511/37917
DOI
https://doi.org/10.1007/978-3-642-15579-6_20
Collections
Department of Computer Engineering, Conference / Seminar
Suggestions
OpenMETU
Core
From extended feature models to constraint logic programming
KARATAS, Ahmet Serkan; Oğuztüzün, Mehmet Halit S.; Dogru, Ali (2013-12-01)
Since feature models for realistic product families may be quite complicated, the automated analysis of feature models is desirable. Although several approaches reported in the literature address this issue, complex cross-tree relationships involving attributes in extended feature models have not been handled. In this article, we introduce a mapping from extended feature models to constraint logic programming over finite domains. This mapping is used to translate into constraint logic programs; basic, cardi...
Transforming cross-tree relations involving attributes into basic constraints in feature models
Karataş, Ahmet Serkan; Oğuztüzün, Mehmet Halit S.; Doǧru, Ali (2011-12-01)
Extended feature models enable expressing powerful constraints by involving feature attributes in cross-tree relations. However, most of the existing methods for the automated analysis of feature models are not devised to handle such models. In this paper we define a transformation to remove such cross-tree relations, which is applicable when certain restrictions hold. This transformation takes an extended feature model with cross-tree relations involving attributes and constructs a semantically equivalent ...
Identifying (Quasi) Equally Informative Subsets in Feature Selection Problems for Classification: A Max-Relevance Min-Redundancy Approach
Karakaya, Gülşah; AHİPAŞAOĞLU, Selin Damla; TAORMİNA, Riccardo (2016-06-01)
An emerging trend in feature selection is the development of two-objective algorithms that analyze the tradeoff between the number of features and the classification performance of the model built with these features. Since these two objectives are conflicting, a typical result stands in a set of Pareto-efficient subsets, each having a different cardinality and a corresponding discriminating power. However, this approach overlooks the fact that, for a given cardinality, there can be several subsets with sim...
Global Constraints on Feature Models
KARATAS, Ahmet Serkan; OGUZTUZUN, Halit; Doğru, Ali Hikmet (2010-09-10)
Feature modeling has been found very effective for modeling and managing variability in Software Product Lines. The nature of feature models invites, sometimes even requires, the use of global constraints. This paper lays the groundwork for the inclusion of global constraints in automated reasoning on feature models. We present a mapping from extended feature models to constraint logic programming over finite domains, and show that this mapping enables using global constraints on feature attributes, as well...
A cluster tree based model selection approach for logistic regression classifier
Tanju, Ozge; Kalaylıoğlu Akyıldız, Zeynep Işıl (Informa UK Limited, 2018-01-01)
Model selection methods are important to identify the best approximating model. To identify the best meaningful model, purpose of the model should be clearly pre-stated. The focus of this paper is model selection when the modelling purpose is classification. We propose a new model selection approach designed for logistic regression model selection where main modelling purpose is classification. The method is based on the distance between the two clustering trees. We also question and evaluate the performanc...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
A. S. KARATAS, H. OGUZTUZUN, and A. H. Doğru, “Mapping Extended Feature Models to Constraint Logic Programming over Finite Domains,” 2010, vol. 6287, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/37917.