Software diversity: practical statistics for its measurement and exploitation

This paper develops, define, illustrate and assess diversity measures, voting strategies for diversity exploitation, and interactions between the two, and introduces 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.

Abstract

The topic of 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.