TEA: A Test Generation Algorithm for Designs with Timing Exceptions Timing exceptions are commonly used to indicate that the timing of certain paths have been relaxed so as to enable the design to meet timing closure. Generating scanbased test patterns without considering timing exceptions can lead to invalid test responses, resulting in unpredictable test quality impact. The existing simulationbased solution masks out unreliable signals after a test pattern is generated. If the signals required for detecting the target fault are unreliable and masked out, the generated test pattern fails to detect the target fault, and it is discarded. To achieve an acceptable test coverage, several iterations of test generation with a randomized decisionmaking process are typically required where different tests are generated for target faults. In this paper, an innovative deterministic ATPG algorithm called TEA (Timing Exception ATPG) is proposed to prevent the generated test patterns from being impacted by timing exceptions. The deterministic algorithm is compatible with the existing simulationbased approach. In this simulation environment, TEA is complete such that for a target fault, the test pattern generated is guaranteed to detect it. If a test pattern cannot be generated using TEA, the target fault is untestable given the timing exception paths in the design and the existing simulation environment. Compared to the existing simulationbased approach, using TEA can generate a more effective test set, improving test coverage, test pattern count, and the total ATPG run time significantly.

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.