Automatic software testing tools are still far from ideal for real world Object-Oriented (OO) Software. The use of nature inspired search algorithms for this problem has been investigated recently. Testing complex data structures (e.g., containers) is very challenging since testing software with simple states is already hard. Because containers are used in almost every type of software, their reliability is of utmost importance. Hence, this paper focuses on the difficulties of testing container classes with nature inspired search algorithms. We will first describe how input data can be automatically generated for testing Java Containers. Input space reductions and a novel testability transformation are presented to aid the search algorithms. Different search algorithms are then considered and studied in order to understand when and why a search algorithm is effective for a testing problem. In our experiments, these nature inspired search algorithms seem to give better results than the traditional techniques described in literature. Besides, the problem of minimising the length of the test sequences is also addressed. Finally, some open research questions are given.

This paper reports on an industrial study of the effectiveness of test data generation. In the literature on the automatic generation of test data a number of techniques stand out as having received a significant amount of interest. One area that has achieved considerable attention is the use of combinatorial techniques to construct data adequate test sets that ensure all pairs, triples etc. of input variables are included in at least one test vector. There has been some systematic evaluation of the technique as applied to unit testing and, while there are indications that the technique can be effective, very little work has been performed using industrial code. Moreover, there has been no comparison of effectiveness of the technique for unit testing compared with tests that are generated by hand. In this paper we apply random and combinatorial (AETG) techniques to a number of functions drawn from industrial code with known faults and existing unit test suites. Results indicate that for simple cases combinatorial techniques can be as effective as the human-generated test, but there are instances associated with complex code where the technique performs poorly—but no worse than randomly generated data. Categories and Subject Descriptors D.2.5 [Testing and Debugging]- Testing tools- data generators,

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Abstract. This paper reports the results of a study comparing the effectiveness of automatically generated tests constructed using random and t-way combinatorial techniques on safety related industrial code using mutation adequacy criteria. A reference point is provided by hand generated test vectors constructed during development to establish minimum acceptance criteria. The study shows that 2-way testing is not adequate measured by mutants kill rate compared with hand generated test set of similar size, but that higher factor t-way test sets can perform at least as well. To reduce the computation overhead of testing large numbers of vectors over large numbers of mutants a staged optimising approach to applying t-way tests is proposed and evaluated which shows improvements in execution time and final test set size.