Computational Intelligence In Software Quality Assurance (Series in Machine Perception & Artifical Intelligence)

Computational Intelligence In Software Quality Assurance (Series in Machine Perception & Artifical Intelligence), 9789812561725 (9812561722), World Scientific Publishing, 2005

Software systems are at once the most complex and the least reliable technological systems human beings construct. A large software system can have over lo2' states, and the reliability of software is infamously poor. Software engineers must usually make assertions about the reliability of software systems after having observed only an insignificant fraction of the possible states of the system. New mechanisms and techniques for inferring the overall quality and reliability of software systems are needed. In this book, we will describe three investigations into the use of computational intelligence and machine learning for software quality assurance, which lead toward such mechanisms.

Our first contribution is the use of chaos theory for software reliability modeling. Software reliability growth models (SRGM) are used to gauge the current and future reliability of a software system. Virtually all current SRGMs assume that software failures occur randomly in time, an assumption that has never been experimentally tested despite being criticized by a number of authors in the field. We have used nonlinear time series analysis to ascertain whether software reliability data from three commercial software projects come from a stochastic process, or from a nonlinear deterministic process. Evidence of deterministic behavior was found in these datasets, lending support to the idea that software failures may be irregular in nature. This is a qualitatively different form of uncertainty than randomness, one that is best modeled using the techniques of fractal sets and chaos theory rather than probability theory.

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