9 research outputs found

    Chalcone derivatives in cancer research and tissue engineering

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    Novel pyrazoline and pyrazole “turn on” fluorescent sensors selective for Zn<sup>2+</sup>/Cd<sup>2+</sup> at <i>λ</i><sub>em</sub> 480 nm and Fe<sup>3+</sup>/Fe<sup>2+</sup> at <i>λ</i><sub>em</sub> 465 nm in MeCN

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    A small series of simple pyrazoline and pyrazole based sensors, all derived from the same chalcone precursors, were synthesised, characterised and screened for their fluorescence “turn on” properties in the presence of multiple metals.</jats:p

    Finding faults: Manual testing vs. random testing+ vs. user reports

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    On the Predictability of Random Tests for Object-Oriented Software

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    Intuition suggests that random testing of object-oriented programs should exhibit a significant difference in the number of faults detected by two different runs of equal duration. As a consequence, random testing would be rather unpredictable. We evaluate the variance of the number of faults detected by random testing over time. We present the results of an empirical study that is based on 1215 hours of randomly testing 27 Eiffel classes, each with 30 seeds of the random number generator. Analyzing over 6 million failures triggered during the experiments, the study provides evidence that the relative number of faults detected by random testing over time is predictable but that different runs of the random test case generator detect different faults. The study also shows that random testing quickly finds faults: the first failure is likely to be triggered within 30 seconds

    Finding faults: Manual testing vs. Random+ testing vs. user reports

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    The usual way to compare testing strategies, whether theoretically or empirically, is to compare the number of faults they detect. To ascertain definitely that a testing strategy is better than another, this is a rather coarse criterion: shouldn't the nature of faults matter as well as their number? The empirical study reported here confirms this conjecture. An analysis of faults detected in Eiffel libraries through three different techniques - random tests, manual tests, and user incident reports - shows that each is good at uncovering significantly different kinds of faults. None of the techniques subsumes any of the others, but each brings distinct contributions
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