Practical iris-based identification systems are easily accessible for data collection at the matching score level. In a typical setting, a video camera is used to collect a single frontal view image of good quality. The image is then preprocessed, encoded, and compared with all entries in the biometric database resulting in a single highest matching score. In this paper, we assume that multiple scans from the same iris are available and design the decision rules based on this assumption. We consider the cases where vectors of matching scores may be described by a Gaussian model with dependent components under both genuine and imposter hypotheses. Two test statistics: the plug-in loglikelihood ratio and the average Hamming distance are designed. We further analyze the performance of filter-based iris recognition systems. The model fit is verified using the Shapiro-Wilk test for normality. We show that the loglikelihood ratio with well-estimated maximum-likelihood parameters in it often outperforms the average Hamming distance statistic. The problem of identification with M iris classes is further stated as an (M+1)ary hypothesis testing problem. We use empirical approach, Chernoff bound, and Large Deviations approach to predict the performance of the iris-based identification system. The bound on the probability of error is evaluated as a function of the number of classes and the number of iris scans per class.

We analyze the performance of an iris based recognition system. We consider a practical setting where matching scores are accessible for collecting data. We assume that multiple scans from the same iris are available and design the decision rules based on this assumption. We show that vectors of matching scores are described by a Gaussian model with dependent components both under the genuine and imposter hypotheses. Two test statistics: the average Hamming distance and the log-likelihood ratio are designed. We show that the log-likelihood ratio with well estimated maximum likelihood parameters in it outperforms the first test statistic. We further use an empirical approach, Chernoff bound, and large deviations approach to predict the performance of the recognition system.

Accurate prediction of fault prone modules (a module is equivalent to a C function or a C+ + method) in software development process enables effective detection and identification of defects. Such prediction models are especially beneficial for large-scale systems, where verification experts need to focus their attention and resources to problem areas in the system under development. This paper presents a novel methodology for predicting fault prone modules, based on random forests. Random forests are an extension of decision tree learning. Instead of generating one decision tree, this methodology generates hundreds or even thousands of trees using subsets of the training data. Classification decision is obtained by voting. We applied random forests in five case studies based on NASA data sets. The prediction accuracy of the proposed methodology is generally higher than that achieved by logistic regression, discriminant analysis and the algorithms in two machine learning software packages, WEKA [I. H. Witten et al. (1999)] and See5. The difference in the performance of the proposed methodology over other methods is statistically significant. Further, the classification accuracy of random forests is more significant over other methods in larger data sets.

This paper describes a novel methodology for predicting fault prone modules. The methodology is based on Dempster-Shafer (D-S) belief networks. Our approach consists of three steps: First, building the Dempster-Shafer network by the induction algorithm; Second, selecting the predictors (attributes) by the logistic procedure; Third, feeding the predictors describing the modules of the current project into the inducted Dempster-Shafer network and identifying fault prone modules. We applied this methodology to a NASA dataset. The prediction accuracy of our methodology is higher than that achieved by logistic regression or discriminant analysis on the same dataset.

To alleviate some of the objections to the basic Jelinski Moranda (JM) model for software failures, Moranda proposed a geometric de-eutrophication model. This model assumes that the times between failures are statistically-independent exponential random variables with given failure rates. In this model the failure rates decrease geometrically with the detection of a fault. Using an intuitive approach, Musa, Iannino, Okumoto , see also Farr , derived expressions for the mean and the intensity functions of the process N (t) which counts the number of faults detected in the time interval [O, t] for the Moranda geometric de-eutrophication model. N (t) is studied as a pure birth stochastic process; its probability generating function is derived, as well as its mean, intensity and reliability functions. The expressions for the mean and intensity functions derived by MIO are only approximations and can be quite different from the true functions for certain choices of the failure rates. The exact expressions for the mean function and the intensity function of N (t) are used to find the optimum release time of software based on a cost structure for Moranda's geometric de-eutrophication model.

The comparison of partition and random sampling methods for software testing has received considerable attention in the literature. A standard criterion for comparisons between random and partition testing based on their expected efficacy in program debugging is the probability of detecting at least one failure causing input in the program's domain. We investigate the relative effectiveness of partition testing versus random testing through the powerful mathematical technique of majorization, which was introduced by Hardy et al. (1952). The tools of majorization and the concepts of Schur (convex and concave) functions (1923) enable us to derive general conditions under which partition testing is superior to random testing and, consequently, to give further insights into the value of partition testing strategies.

Automated generation of test cases is a prerequisite for fast testing. Whereas the research in automated test data generation addressed the creation of individual test points, test trajectory generation has attracted limited attention. In simple terms, a test trajectory is defined as a series of data points, with each (possibly multidimensional) point relying upon the value(s) of previous point(s). Many embedded systems use data trajectories as inputs, including closed-loop process controllers, robotic manipulators, nuclear monitoring systems, and flight control systems. For these systems, testers can either handcraft test trajectories, use input trajectories from older versions of the system or, perhaps, collect test data in a high fidelity system simulator. While these are valid approaches, they are expensive and time-consuming, especially if the assessment goals require many tests. We developed a framework for expanding a small, conventionally developed set of test trajectories into a large set suitable, for example, for system safety assurance. Statistical regression is the core of this framework. The regression analysis builds a relationship between controllable independent variables and closely correlated dependent variables, which represent test trajectories. By perturbing the independent variables, new test trajectories are generated automatically. Our approach has been applied in the safety assessment of a fault tolerant flight control system. Linear regression, multiple linear regression, and autoregressive techniques are compared. The performance metrics include the speed of test generation and the percentage of "acceptable" trajectories, measured by the domain specific reasonableness checks.

It is generally believed that component-based software development leads to improved application quality, maintainability and reliability. However most software reliability techniques model integrated systems. These models disregard system's internal structure, taking into account only the failure data and interactions with the environment. We propose a novel approach to reliability analysis of component-based systems. Reliability prediction algorithm allows system architects to analyze reliability of the system before it is built, taking into account component reliability estimates and their anticipated usage. Fully integrated with the UML, this step can guide the process of identifying critical components and analyze the effect of replacing them with the more/less reliable ones. Reliability assessment algorithm, applicable in the system test phase, utilizes these reliability predictions as prior probabilities. In the Bayesian estimation. framework, posterior probability of failure is calculated from the priors and test failure data.

The behavior of nondeterminate systems can be hard to predict, since similar inputs at different times can generate different outputs. In other words, the behavior seen during the testing process may not be seen at runtime. Due to the uncertainties associated with nondeterminism, the standard view is that we should avoid such nondeterminate systems, especially for systems requiring high reliability. While this is a valid guideline, at least in two application areas such nondeterminacy is unavoidable. Early life-cycle requirements and AI software are becoming widely used, yet both are imprecise and may exhibit nondeterminate behaviour if explored rigorously by a test device. Based on a literature review and some theoretical studies, we argue that many stable properties exist within the space of all possible nondeterminate behaviors. However, we also show that seemingly trivial changes to a nondeterministic system can turn an easily testable system into an impossibly hard system to test. Finally, we stress that this analysis does not imply a correlation between stable zones of nondeterminate testability and the ultimate maintainability of nondeterminate systems. That is, while we are optimistic about testing nondeterminate systems, we remain cautious about the maintenance of such systems.