2,529 research outputs found
Submodular relaxation for inference in Markov random fields
In this paper we address the problem of finding the most probable state of a
discrete Markov random field (MRF), also known as the MRF energy minimization
problem. The task is known to be NP-hard in general and its practical
importance motivates numerous approximate algorithms. We propose a submodular
relaxation approach (SMR) based on a Lagrangian relaxation of the initial
problem. Unlike the dual decomposition approach of Komodakis et al., 2011 SMR
does not decompose the graph structure of the initial problem but constructs a
submodular energy that is minimized within the Lagrangian relaxation. Our
approach is applicable to both pairwise and high-order MRFs and allows to take
into account global potentials of certain types. We study theoretical
properties of the proposed approach and evaluate it experimentally.Comment: This paper is accepted for publication in IEEE Transactions on
Pattern Analysis and Machine Intelligenc
Fuzzy Adaptive Tuning of a Particle Swarm Optimization Algorithm for Variable-Strength Combinatorial Test Suite Generation
Combinatorial interaction testing is an important software testing technique
that has seen lots of recent interest. It can reduce the number of test cases
needed by considering interactions between combinations of input parameters.
Empirical evidence shows that it effectively detects faults, in particular, for
highly configurable software systems. In real-world software testing, the input
variables may vary in how strongly they interact, variable strength
combinatorial interaction testing (VS-CIT) can exploit this for higher
effectiveness. The generation of variable strength test suites is a
non-deterministic polynomial-time (NP) hard computational problem
\cite{BestounKamalFuzzy2017}. Research has shown that stochastic
population-based algorithms such as particle swarm optimization (PSO) can be
efficient compared to alternatives for VS-CIT problems. Nevertheless, they
require detailed control for the exploitation and exploration trade-off to
avoid premature convergence (i.e. being trapped in local optima) as well as to
enhance the solution diversity. Here, we present a new variant of PSO based on
Mamdani fuzzy inference system
\cite{Camastra2015,TSAKIRIDIS2017257,KHOSRAVANIAN2016280}, to permit adaptive
selection of its global and local search operations. We detail the design of
this combined algorithm and evaluate it through experiments on multiple
synthetic and benchmark problems. We conclude that fuzzy adaptive selection of
global and local search operations is, at least, feasible as it performs only
second-best to a discrete variant of PSO, called DPSO. Concerning obtaining the
best mean test suite size, the fuzzy adaptation even outperforms DPSO
occasionally. We discuss the reasons behind this performance and outline
relevant areas of future work.Comment: 21 page
Phase Transitions of the Typical Algorithmic Complexity of the Random Satisfiability Problem Studied with Linear Programming
Here we study the NP-complete -SAT problem. Although the worst-case
complexity of NP-complete problems is conjectured to be exponential, there
exist parametrized random ensembles of problems where solutions can typically
be found in polynomial time for suitable ranges of the parameter. In fact,
random -SAT, with as control parameter, can be solved quickly
for small enough values of . It shows a phase transition between a
satisfiable phase and an unsatisfiable phase. For branch and bound algorithms,
which operate in the space of feasible Boolean configurations, the empirically
hardest problems are located only close to this phase transition. Here we study
-SAT () and the related optimization problem MAX-SAT by a linear
programming approach, which is widely used for practical problems and allows
for polynomial run time. In contrast to branch and bound it operates outside
the space of feasible configurations. On the other hand, finding a solution
within polynomial time is not guaranteed. We investigated several variants like
including artificial objective functions, so called cutting-plane approaches,
and a mapping to the NP-complete vertex-cover problem. We observed several
easy-hard transitions, from where the problems are typically solvable (in
polynomial time) using the given algorithms, respectively, to where they are
not solvable in polynomial time. For the related vertex-cover problem on random
graphs these easy-hard transitions can be identified with structural properties
of the graphs, like percolation transitions. For the present random -SAT
problem we have investigated numerous structural properties also exhibiting
clear transitions, but they appear not be correlated to the here observed
easy-hard transitions. This renders the behaviour of random -SAT more
complex than, e.g., the vertex-cover problem.Comment: 11 pages, 5 figure
Maximum Persistency via Iterative Relaxed Inference with Graphical Models
We consider the NP-hard problem of MAP-inference for undirected discrete
graphical models. We propose a polynomial time and practically efficient
algorithm for finding a part of its optimal solution. Specifically, our
algorithm marks some labels of the considered graphical model either as (i)
optimal, meaning that they belong to all optimal solutions of the inference
problem; (ii) non-optimal if they provably do not belong to any solution. With
access to an exact solver of a linear programming relaxation to the
MAP-inference problem, our algorithm marks the maximal possible (in a specified
sense) number of labels. We also present a version of the algorithm, which has
access to a suboptimal dual solver only and still can ensure the
(non-)optimality for the marked labels, although the overall number of the
marked labels may decrease. We propose an efficient implementation, which runs
in time comparable to a single run of a suboptimal dual solver. Our method is
well-scalable and shows state-of-the-art results on computational benchmarks
from machine learning and computer vision.Comment: Reworked version, submitted to PAM
Practical Pairwise Testing for Software Product Lines
International audienceOne key challenge for software product lines is efficiently managing variability throughout their lifecycle. In this paper, we address the problem of variability in software product lines testing. We (1) identify a set of issues that must be addressed to make software product line testing work in practice and (2) provide a framework that combines a set of techniques to solve these issues. The framework integrates feature modelling, combinatorial interaction testing and constraint programming techniques. First, we extract variability in a software product line as a feature model with specified feature interdependencies. We then employ an algorithm that generates a minimal set of valid test cases covering all 2-way feature interactions for a given time interval. Furthermore, we evaluate the framework on an industrial SPL and show that using the framework saves time and provides better test coverage. In particular, our experiments show that the framework improves industrial testing practice in terms of (i) 17% smaller set of test cases that are (a) valid and (b) guarantee all 2-way feature coverage (as opposite to 19.2% 2-way feature coverage in the hand made test set), and (ii) full flexibility and adjustment of test generation to available testing time
A Survey of Constrained Combinatorial Testing
Combinatorial Testing (CT) is a potentially powerful testing technique,
whereas its failure revealing ability might be dramatically reduced if it fails
to handle constraints in an adequate and efficient manner. To ensure the wider
applicability of CT in the presence of constrained problem domains, large and
diverse efforts have been invested towards the techniques and applications of
constrained combinatorial testing. In this paper, we provide a comprehensive
survey of representations, influences, and techniques that pertain to
constraints in CT, covering 129 papers published between 1987 and 2018. This
survey not only categorises the various constraint handling techniques, but
also reviews comparatively less well-studied, yet potentially important,
constraint identification and maintenance techniques. Since real-world programs
are usually constrained, this survey can be of interest to researchers and
practitioners who are looking to use and study constrained combinatorial
testing techniques
CMSA algorithm for solving the prioritized pairwise test data generation problem in software product lines.
In Software Product Lines, it may be difficult or even impossible to test all the products of the family because of the large number of valid feature combinations that may exist (Ferrer et al. in: Squillero, Sim (eds) EvoApps 2017, LNCS 10200, Springer, The Netherlands, pp 3–19, 2017). Thus, we want to find a minimal subset of the product family that allows us to test all these possible combinations (pairwise). Furthermore, when testing a single product is a great effort, it is desirable to first test products composed of a set of priority features. This problem is called Prioritized Pairwise Test Data Generation Problem. State-of-the-art algorithms based on Integer Linear Programming for this problem are faster enough for small and medium instances. However, there exists some real instances that are too large to be computed with these algorithms in a reasonable time because of the exponential growth of the number of candidate solutions. Also, these heuristics not always lead us to the best solutions. In this work we propose a new approach based on a hybrid metaheuristic algorithm called Construct, Merge, Solve & Adapt. We compare this matheuristic with four algorithms: a Hybrid algorithm based on Integer Linear Programming, a Hybrid algorithm based on Integer Nonlinear Programming, the Parallel Prioritized Genetic Solver, and a greedy algorithm called prioritized-ICPL. The analysis reveals that CMSA is statistically significantly better in terms of quality of solutions in most of the instances and for most levels of weighted coverage, although it requires more execution time.This research has been partially funded by the Spanish Ministry of Economy and Competitiveness (MINECO) and the European Regional Development Fund (FEDER) under contract TIN2017-88213-R (6city project), the University of Málaga, Consejerıa de Economıa y Conocimiento de la Junta de Andalucıa and FEDER under contract UMA18-FEDERJA-003 (PRECOG project), the Ministry of Science, Innovation and Universities and FEDER under contract RTC-2017-6714-5 (ECOIoT project), the H2020 European Project Tailor (H2020-ICT-2019-3), the Spanish SBSE Research Network (RED2018-102472-T), and the University of Málaga under contract PPIT-UMA-B1-2017/07 (EXHAURO Project). J. Ferrer thanks University of Mállaga for his postdoc fellowship
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