PROJECT TITLE :

Measuring the Diversity of a Test Set With Distance Entropy

ABSTRACT:

Most existing metrics that we decision white-box metrics, such as coverage metrics, need white-box info, like program structure info, and historical runtime info, to guage the fault detection capability of a check set. In practice, such white-box data is sometimes unavailable or tough to get, that suggests that they usually can not be used. In this paper, we have a tendency to propose a black-box metric, distance entropy, based on the diversification plan behind many revealed diversity-based techniques. Distance entropy provides a doable resolution for check set analysis when white-box information isn't obtainable. The empirical study illustrates that distance entropy can effectively evaluate take a look at sets if the space metric between tests is well outlined. Meanwhile, distance entropy outperforms straightforward diversity metrics without increasing time complexity.


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