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» Constraint Models for the Covering Test Problem
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STOC
2004
ACM
150views Algorithms» more  STOC 2004»
14 years 9 months ago
Typical properties of winners and losers in discrete optimization
We present a probabilistic analysis for a large class of combinatorial optimization problems containing, e.g., all binary optimization problems defined by linear constraints and a...
René Beier, Berthold Vöcking
KBSE
2005
IEEE
14 years 2 months ago
Automated test generation for engineering applications
In test generation based on model-checking, white-box test criteria are represented as trap conditions written in a temporal logic. A model checker is used to refute trap conditio...
Songtao Xia, Ben Di Vito, César Muño...
VAMOS
2010
Springer
13 years 10 months ago
Introducing TVL, a Text-based Feature Modelling
Feature models are a common way to represent variability in software product line engineering. For this purpose, most authors use a graphical notation based on FODA. The main drawb...
Quentin Boucher, Andreas Classen, Paul Faber, Patr...

Book
5396views
15 years 7 months ago
Markov Random Field Modeling in Computer Vision
Markov random field (MRF) theory provides a basis for modeling contextual constraints in visual processing and interpretation. It enables us to develop optimal vision algorithms sy...
Stan Z. Li
KI
2004
Springer
14 years 2 months ago
Model Based Deduction for Database Schema Reasoning
Abstract. We aim to demonstrate that automated deduction techniques, in particular those following the model computation paradigm, are very well suited for database schema/query re...
Peter Baumgartner, Ulrich Furbach, Margret Gro&szl...