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The oracle problem remains one of the key challenges in software testing, for which little automated support has been developed so far. In my thesis work we introduce a technique for assessing and improving test oracles by reducing the incidence of both false positives and false negatives. Our technique combines test case generation to reveal false positives and mutation testing to reveal false negatives. The experimental results on five real-world subjects show that the fault detection rate of the oracles after improvement increases, on average, by 48.6% (86% over the implicit oracle). Three actual, exposed faults in the studied systems were subsequently confirmed and fixed by the developers. However, our technique contains a human in the loop, which was represented only by the author during the initial experiments. Our next goal is to conduct further experiments where the human in the loop will be represented …