| Summary: | A good strategy to test a software component involves the generation of the whole
set of cases that participate in its operation. While testing only individual values
may not be enough, exhaustive testing of all possible combinations is not always
feasible. An alternative technique to accomplish this goal is called combinato-
rial testing. Combinatorial testing is a method that can reduce cost and increase
the effectiveness of software testing for many applications. It is based on con-
structing functional test-suites of economical size, which provide coverage of the
most prevalent configurations. Covering arrays are combinatorial objects, that
have been applied to do functional tests of software components. The use of cov-
ering arrays allows to test all the interactions, of a given size, among the input
parameters using the minimum number of test cases.
For software testing, the fundamental problem is finding a covering array with
the minimum possible number of rows, thus reducing the number of tests, the
cost, and the time expended on the software testing process. Because of the
importance of the construction of (near) optimal covering arrays, much research
has been carried out in developing effective methods for constructing them. There
are several reported methods for constructing these combinatorial models, among
them are: (1) algebraic methods, recursive methods, (3) greedy methods, and (4)
metaheuristics methods.
Metaheuristic methods, particularly through the application of simulated anneal-
ing has provided the most accurate results in several instances to date. Simulated
annealing algorithm is a general-purpose stochastic optimization method that has
proved to be an effective tool for approximating globally optimal solutions to many
optimization problems. However, one of the major drawbacks of the simulated an-
nealing is the time it requires to obtain good solutions.
In this thesis, we propose the development of an improved simulated annealing
algorithm === Avila George, H. (2012). Constructing Covering Arrays using Parallel Computing and Grid Computing [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/17027 === Palancia
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