CAJ | 학술논문

在分析软件失效机理后认为：有些软件失效行为具有混沌性，所以可以用混沌方法来处理其软件可靠性推断问题.但在应用混沌方法前先要进行系统辨识，确定为混沌系统后，才能应用嵌入空间技术从软件失效时间序列重建系统相空间和吸引子，进而用吸引子所揭示的混沌属性来估计软件可靠性.文中在三个标准数据集的基础上对此进行了实证分析，结果表明其中两个数据集源于混沌机制，他们的吸引子具有低维的小数极限维数，而且预测与实际可靠性吻合较好.值得指出的是文中所提混沌方法突破了软件可靠性一贯使用随机分析的局限.
재분석연건실효궤리후인위：유사연건실효행위구유혼돈성，소이가이용혼돈방법래처리기연건가고성추단문제.단재응용혼돈방법전선요진행계통변식，학정위혼돈계통후，재능응용감입공간기술종연건실효시간서렬중건계통상공간화흡인자，진이용흡인자소게시적혼돈속성래고계연건가고성.문중재삼개표준수거집적기출상대차진행료실증분석，결과표명기중량개수거집원우혼돈궤제，타문적흡인자구유저유적소수겁한유수，이차예측여실제가고성문합교호.치득지출적시문중소제혼돈방법돌파료연건가고성일관사용수궤분석적국한.
Computers affected almost every aspect of human lives. As thedependency 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.