6,084 research outputs found

    Some conservative stopping rules for the operational testing of safety-critical software

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    Operational testing, which aims to generate sequences of test cases with the same statistical properties as those that would be experienced in real operational use, can be used to obtain quantitative measures of the reliability of software. In the case of safety critical software it is common to demand that all known faults are removed. This means that if there is a failure during the operational testing, the offending fault must be identified and removed. Thus an operational test for safety critical software takes the form of a specified number of test cases (or a specified period of working) that must be executed failure-free. This paper addresses the problem of specifying the numbers of test cases (or time periods) required for a test, when the previous test has terminated as a result of a failure. It has been proposed that, after the obligatory fix of the offending fault, the software should be treated as if it were completely novel, and be required to pass exactly the same test as originally specified. The reasoning here claims to be conservative, inasmuch as no credit is given for any previous failure-free operation prior to the failure that terminated the test. We show that, in fact, this is not a conservative approach in all cases, and propose instead some new Bayesian stopping rules. We show that the degree of conservatism in stopping rules depends upon the precise way in which the reliability requirement is expressed. We define a particular form of conservatism that seems desirable on intuitive grounds, and show that the stopping rules that exhibit this conservatism are also precisely the ones that seem preferable on other grounds

    Design diversity: an update from research on reliability modelling

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    Diversity between redundant subsystems is, in various forms, a common design approach for improving system dependability. Its value in the case of software-based systems is still controversial. This paper gives an overview of reliability modelling work we carried out in recent projects on design diversity, presented in the context of previous knowledge and practice. These results provide additional insight for decisions in applying diversity and in assessing diverseredundant systems. A general observation is that, just as diversity is a very general design approach, the models of diversity can help conceptual understanding of a range of different situations. We summarise results in the general modelling of common-mode failure, in inference from observed failure data, and in decision-making for diversity in development.

    A Bayesian modification to the Jelinski-Moranda software reliability growth model

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    The Jelinski-Moranda (JM) model for software reliability was examined. It is suggested that a major reason for the poor results given by this model is the poor performance of the maximum likelihood method (ML) of parameter estimation. A reparameterization and Bayesian analysis, involving a slight modelling change, are proposed. It is shown that this new Bayesian-Jelinski-Moranda model (BJM) is mathematically quite tractable, and several metrics of interest to practitioners are obtained. The BJM and JM models are compared by using several sets of real software failure data collected and in all cases the BJM model gives superior reliability predictions. A change in the assumption which underlay both models to present the debugging process more accurately is discussed

    The problems of assessing software reliability ...When you really need to depend on it

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    This paper looks at the ways in which the reliability of software can be assessed and predicted. It shows that the levels of reliability that can be claimed with scientific justification are relatively modest

    Validation of Ultrahigh Dependability for Software-Based Systems

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    Modern society depends on computers for a number of critical tasks in which failure can have very high costs. As a consequence, high levels of dependability (reliability, safety, etc.) are required from such computers, including their software. Whenever a quantitative approach to risk is adopted, these requirements must be stated in quantitative terms, and a rigorous demonstration of their being attained is necessary. For software used in the most critical roles, such demonstrations are not usually supplied. The fact is that the dependability requirements often lie near the limit of the current state of the art, or beyond, in terms not only of the ability to satisfy them, but also, and more often, of the ability to demonstrate that they are satisfied in the individual operational products (validation). We discuss reasons why such demonstrations cannot usually be provided with the means available: reliability growth models, testing with stable reliability, structural dependability modelling, as well as more informal arguments based on good engineering practice. We state some rigorous arguments about the limits of what can be validated with each of such means. Combining evidence from these different sources would seem to raise the levels that can be validated; yet this improvement is not such as to solve the problem. It appears that engineering practice must take into account the fact that no solution exists, at present, for the validation of ultra-high dependability in systems relying on complex software

    Dynamic learning with neural networks and support vector machines

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    Neural network approach has proven to be a universal approximator for nonlinear continuous functions with an arbitrary accuracy. It has been found to be very successful for various learning and prediction tasks. However, supervised learning using neural networks has some limitations because of the black box nature of their solutions, experimental network parameter selection, danger of overfitting, and convergence to local minima instead of global minima. In certain applications, the fixed neural network structures do not address the effect on the performance of prediction as the number of available data increases. Three new approaches are proposed with respect to these limitations of supervised learning using neural networks in order to improve the prediction accuracy.;Dynamic learning model using evolutionary connectionist approach . In certain applications, the number of available data increases over time. The optimization process determines the number of the input neurons and the number of neurons in the hidden layer. The corresponding globally optimized neural network structure will be iteratively and dynamically reconfigured and updated as new data arrives to improve the prediction accuracy. Improving generalization capability using recurrent neural network and Bayesian regularization. Recurrent neural network has the inherent capability of developing an internal memory, which may naturally extend beyond the externally provided lag spaces. Moreover, by adding a penalty term of sum of connection weights, Bayesian regularization approach is applied to the network training scheme to improve the generalization performance and lower the susceptibility of overfitting. Adaptive prediction model using support vector machines . The learning process of support vector machines is focused on minimizing an upper bound of the generalization error that includes the sum of the empirical training error and a regularized confidence interval, which eventually results in better generalization performance. Further, this learning process is iteratively and dynamically updated after every occurrence of new data in order to capture the most current feature hidden inside the data sequence.;All the proposed approaches have been successfully applied and validated on applications related to software reliability prediction and electric power load forecasting. Quantitative results show that the proposed approaches achieve better prediction accuracy compared to existing approaches

    Worst Case Reliability Prediction Based on a Prior Estimate of Residual Defects

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    In this paper we extend an earlier worst case bound reliability theory to derive a worst case reliability function R(t), which gives the worst case probability of surviving a further time t given an estimate of residual defects in the software N and a prior test time T. The earlier theory and its extension are presented and the paper also considers the case where there is a low probability of any defect existing in the program. For the "fractional defect" case, there can be a high probability of surviving any subsequent time t. The implications of the theory are discussed and compared with alternative reliability models
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