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In development processes with high code production rates testing typically triggers fault diagnosis to localize the detected failures. However, current test prioritization algorithms are tuned for failure detection rate rather than diagnostic information. Consequently, unnecessary diagnostic effort might be spent to localize the faults. This paper presents a dynamic test prioritization algorithm that trades fault detection rate for diagnostic performance, minimizing overall testing and diagnosis cost. The algorithm exploits pass/fail information from each test to select the next test, optimizing the diagnostic information produced per test. Experimental results from synthetic test suites, and suites taken from the Software-artifact Infrastructure Repository show possible diagnostic cost reductions up to 10 and 19 percent, respectively, compared to the best of random selection, FEP, and ART.
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