نبذة مختصرة : A good strategy to test a software component involves the generation of the wholeset of cases that participate in its operation. While testing only individual valuesmay not be enough, exhaustive testing of all possible combinations is not alwaysfeasible. An alternative technique to accomplish this goal is called combinato-rial testing. Combinatorial testing is a method that can reduce cost and increasethe effectiveness of software testing for many applications. It is based on con-structing functional test-suites of economical size, which provide coverage of themost prevalent configurations. Covering arrays are combinatorial objects, thathave 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 inputparameters using the minimum number of test cases.For software testing, the fundamental problem is finding a covering array withthe minimum possible number of rows, thus reducing the number of tests, thecost, and the time expended on the software testing process. Because of theimportance of the construction of (near) optimal covering arrays, much researchhas been carried out in developing effective methods for constructing them. Thereare several reported methods for constructing these combinatorial models, amongthem 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. Simulatedannealing algorithm is a general-purpose stochastic optimization method that hasproved to be an effective tool for approximating globally optimal solutions to manyoptimization 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 annealingalgorithm
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