this paper is the exploitation of diversity to enhance computer system reliability. It is well-established that a diverse system composed of multiple alternative versions is more reliable than any single version alone, and this knowledge has occasionally been exploited in safety-critical applications. However, it is not clear what this property is, nor how the available diversity in a collection of versions is best exploited. We develop, define, illustrate and assess diversity measures, voting strategies for diversity exploitation, and interactions between the two. We take the view that a proper understanding of such issues is required if multiversion software engineering is to be elevated from the current `try it and see' procedure to a systematic technology. In addition, we introduce inductive programming techniques, particularly neural computing, as a cost-effective route to the practical use of multiversion systems outside the demanding requirements of safety-critical systems --- i.e. in general software engineering. software diversity, multiversion systems, diversity measures, voting strategies, neural computing, inductive programming

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