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& One of the most important issues in software testing is the generation of the input cases used during the test. Due to the expensive cost of this task, its automation has become a key aspect. An alternative to obtain this is evolutionary testing. The aim of evolutionary testing is the creation of test data by means of combinatorial optimization search methods.A heuristic approach to the automatic generation of test cases is presented. The developed approach makes use of an emerging set of evolutionary algorithms called estimation of distribution algorithms. The analysis of the experimental results obtained presents this set of optimization techniques as a promising option for tackling this problem. More precisely, the performance of different estimation of distribution algorithms is evaluated and a comparison with the results of previous works is carried out.Testing is a crucial part of the software development process (Beizer 1990). It plays a main role in the search for the quality required as it constitutes the primary way used in practice to verify the correct behavior of the software produced. In fact, it is not unusual to dedicate at least 50% of the project resources to this phase (Boehm 1981).A major activity in testing is test design. Within this activity, a key task is the generation of the input cases to be applied to the program under test. This is a critical task, as the produced input must be adequate to the test type and its requirements. This, together with the fact that in most organizations this generation is performed manually, results in a high amount of resources dedicated to the test data generation step. Thus, the automatic

Abstract-One of the main tasks software testing involves is the generation of the test cases to be used during the test. Due to its expensive cost, the automation of this task has become one of the key issues in the area. While most of the work on test data generation has concentrated on procedural software, little attention has been paid to object oriented programs, even so they are a usual practice nowadays.We present an approach based on Estimation of Distribution Algorithms (EDAs) for dealing with the test data generation of a particular type of objects, that is, containers. This is the first time that an EDA has been applied to testing object oriented software. In addition to automated test data generation, the EDA approach also offers the potential of modelling the fitness landscape defined by the testing problem and thus could provide some insight into the problem. Firstly, we show results from empirical evaluations and comment on some appealing properties of EDAs in this context. Next, a framework is discussed in order to deal with the generation of efficient tests for the container classes. Preliminary results are provided as well.

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