An approach for clustering test data

Abstract

The existing test techniques and criteria are considered complementary because they can reveal different kinds of faults and test specific aspects of the program. The functional criteria, such as Category Partition, are difficult to be automated, and are usually manually applied. Structural and fault-based testing criteria generally provide measures to evaluate the test data being used. The existing supporting tools produce a lot of information including: input and produced output, structural coverage, mutation score, faults revealed, etc. However, such information is not linked to functional aspects of the software. In this work, we present an approach based on machine learning clustering techniques that uses the test results for clustering test data. It allows the automation of functional test, since the obtained clusters can be considered equivalence classes. In addition to this, they relate test results from the application of different test techniques. A study case is described, and the use of the clusters is illustrated during regression test for ordering and reduction of test sets.