A Chaotic Model for Software Reliability
Feng Zou · Chinese Journal of Computers · 2001
Computers affected almost every aspect of human lives. As the dependency on computer systems of human beings grows, so does the need for the technology of reliability of computer systems. In contrast to computer hardware, software is far more complicated. Thus the key is to improve the reliability of software if the overall reliability of a system is to be improved. Although scientists, in the past few decades, proposed lots of reliability models for software, which greatly enhanced the reliability and productivity of software products, these models are far from satisfactory. To build models of high accuracy and to improve the existing models is therefore of practical significance. Conventional theory of software reliability assumes that the failure processes of software are completely random, whereas authors of this paper, on the basis of careful investigation on physical mechanics of software failures,suggest that some dynamics of software failures are of chaotic features. Thus the reliability issue of these systems can be addressed with chaotic approaches. But before applying chaotic methodology to estimate the reliability of the software under consideration, the first thing to do is system identification that uses certain standards to distinguish chaotic dynamics from stochastic ones. In cases of chaos, the technology of embedding space is employed to reconstruct, from a time series of failures, the phase space and the attractor which reveals the chaotic properties that are further used to assess the reliability of the software product of interest. Based on three data sets including two of Musa's, the empirical study indicates that two of the data sets arise from chaotic dynamics rather than from stochastic ones, as the reconstructed attractors have low and limiting fractional dimensions. Interestingly, the predictions using chaotic methods in such cases fit quite well with actual reliabilities. This phenomenon is worth particular notice because it goes beyond the conventional limitation of stochastic assumptions of reliability analysis. To sum up, this paper proposed a chaotic model for software reliability. The validity, feasibility and applicability of the model are verified theoretically and experimentally.