Propp & Wilson (1996) described a protocol, called coupling from the past, for exact sampling from a target distribution using a coupled Markov chain Monte Carlo algorithm. In this paper we extend coupling from the past to various MCMC samplers on a continuous state space; rather than following the monotone sampling device of Propp & Wilson, our approach uses methods related to gamma-coupling and rejection sampling to simulate the chain, and direct accounting of sample paths.

Mutants are automatically-generated, possibly faulty variants of programs. The mutation adequacy ratio of a test suite is the ratio of non-equivalent mutants it is able to identify to the total number of non-equivalent mutants. This ratio can be used as a measure of test effectiveness. However, it can be expensive to calculate, due to the large number of different mutation operators that have been proposed for generating the mutants.In this paper, we address the problem of finding a small set of mutation operators which is still sufficient for measuring test effectiveness. We do this by defining a statistical analysis procedure that allows us to identify such a set, together with an associated linear model that predicts mutation adequacy with high accuracy. We confirm the validity of our procedure through cross-validation and the application of other, alternative statistical analyses.

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