Optimal improvement of the lower bound performance of partition testing strategies

Although partition testing strategies are intuitively more appealing than random testing, previous empirical and analytical studies show that under unfavourable circumstances partition testing can be very ineffective. The problem of maximally improving the lower bound performance of partition testing by the choice of appropriate test distributions is investigated. An algorithm that generates optimal test distributions for this purpose is proposed and analysed. Moreover, the algorithm can also serve to systematically approximate the proportional sampling strategy, which has previously been proved to be at least as good as random testing