We provide both theoretical analysis and

empirical evidence that a small constant sample of mutants yields

statistically similar results to running a full mutation analysis,

regardless of the size of the program or similarity between

mutants. We show that a similar approach, using a constant

sample of inputs can estimate the degree of stubbornness in

mutants remaining to a high degree of statistical confidence,

and provide a mutation analysis framework for Python that

incorporates the analysis of stubbornness of mutants.

July 12, 2015