Research on Test Suite Reduction Using Attribute Relevance Analysis

Essence of software testing is to choose a representative value (known as test case) from the input to perform the programs under test. The actual results of the programs will be checked to verify the consistency with the expected ones. If the results are different, it should take some correction and adjustment correspondingly. The existing method for test suite generation is mainly based on the test requirements which are related to the given testing objectives. It generates the test suites directly. Inevitably, some data in those suites may be redundant and need to be reduced. In fact, test case selection is to make effective partition within the input, and then generate the cases of high-performance. Based on the conception of partition, this paper presents a method of test suite reduction by using data classification techniques which are introduced in data mining. This method tries to use attribute relevance analysis to find the interrelations of all attributes in test requirements, and then reduce the test suite with the most appropriate attributes and values.