Download Statistical Methods in Software Engineering: Reliability and by Nozer D. Singpurwalla PDF

By Nozer D. Singpurwalla

This preface relates to 3 concerns that we'd wish to convey to the eye of the readers: our targets, our meant viewers, and the character of the fabric. we've got in brain numerous pursuits. the 1st is to set up a framework for facing uncertainties in software program engineering, and for utilizing quantitative measures for determination making during this context. the second one is to carry into standpoint the massive physique of labor having statistical content material that's appropriate to software program engineering, that may now not have seemed within the conventional shops dedicated to it. attached with this moment aim is a wish to streamline and manage our personal considering and paintings during this zone. Our 3rd aim is to supply a platform that allows an interface among desktop scientists and statisticians to handle a category of difficulties in laptop technological know-how. it seems that such an interface is critical to supply the wanted synergism for fixing a few tricky difficulties that the topic poses. Our ultimate aim is to function an agent for exciting extra cross-disciplinary study in computing device technology and facts. To what volume the cloth the following will meet our goals can merely be assessed with the passage of time. Our meant viewers is desktop scientists, software program engineers, and reliability analysts, who've a few publicity to likelihood and statistics. utilized statisticians attracted to reliability difficulties also are a section of our meant audience.

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Extra resources for Statistical Methods in Software Engineering: Reliability and Risk

Sample text

3 Moments of Probability Distributions and Expectation of Random Variables Moments and expected values are convenient ways of summarlzlOg probability models. Indeed some of the most commonly used statistics in day-today operations have their genesis in the notion of moments. Examples are the mean, the variance, the correlation, the mean time to failure, and so on. Such statistics are often the mainstay of much of the data analyses done in software engineering. The aim of this subsection is to put the commonly used statistics in their proper perspective.

A more pragmatic reason is that we are able to generate skewed distributions by suitable transformations of the Gaussian. For example, if X is a lifetime, and if it is reasonable to assume [cf. 5). The probability density function of a lognormal distribution function is 1 (Jogx20"2- f(x 1 p. L)2 ) • for °< x < 00. 2 Probability Models and Their Parameters 39 The Truncated Normal Distribution Another skewed distribution, which is derived from the Gaussian and which has applications in software quality assessment is the truncated normal distribution.

A*( • )) = 1; and for any 0 ::::; s < t, the number of events that occur in [s,t] has a Poisson distribution with a parameter (A *(t) - A*(s)); that is, for k = 0, 1, 2, ... , = k! )) = (A*(t)~t*(S»k exp( - (A*(t) - A*(s))). ), then a knowledge of the past behavior of the process is irrelevant for our assessment of uncertainty about future occurrences. ), the number of events occurring in disjoint time intervals are independent random variables. This property is known as the independent increments property, and is a defining characteristic of all Poisson process models.

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