An Evaluation of Test Suite Minimization Techniques As a software project evolves over time, the associated test suite usually grows with it. If test suites are not carefully maintained, this can easily result in massive test execution duration, reducing the benefits of regression testing because faults are found later in development or even after release. Test suite minimization aims to combat long running test suites by removing redundant test cases. Previous work mainly evaluates test suite minimization techniques based on comparably small projects, which are less practically relevant. In this paper, we compare four test suite minimization techniques by applying them to several open source software projects and evaluate the results. We find that the size and execution time of all the test suites can be reduced by over 70% on average. However, there is a substantial loss in fault detection capability of, on average, around 12.5%, restricting the applicability of this form of test suite minimization.

Generating Reduced Tests for FSMs using a SearchBased Testing Approach Ideally, in FSMbased testing, a test suite should have as few input symbols and sequences as possible, speeding up its execution, and being able to reveal as many faulty implementations as possible in a given fault domain. Unfortunately, in general, it is an NPhard problem. Several test generation methods have been proposed to generate a test suite with full fault detection capability. However, they usually generate large test suites with many sequences. In this paper, we propose a searchbased software testing method aiming to generate test suites that represent a good tradeoff between its cost and its fault coverage. A new encoding, called Nodedepthoutdegree Encoding (NDOE) and its mutation and initialization operators are proposed. Also, a new fitness function is presented. The proposed fitness is composed of three objectives: minimize the test suite length, minimize the number of test sequences, and maximize the fault coverage, which analysis is based on mutation score. The experimental results show that the proposed method is able to generate lower cost test suites, with high mutation score, than literature test generation methods.