A survey of maintenance policies of deteriorating systems

Mathematica--A System for Doing Mathematics by Computer.

This paper reviews the relevant literature on the problem of determining suitable ordering policies for both fixed life perishable inventory, and inventory subject to continuous exponential decay. We consider both deterministic and stochastic demand for single and multiple products. Both optimal and sub-optimal order policies are discussed. In addition, a brief review of the application of these models to blood bank management is included. The review concludes with a discussion of some of the interesting open research questions in the area.

Perishable Inventory Theory: A Review

Technical foundations introduction software reliability and system reliability the operational profile software reliability modelling survey model evaluation and recalibration techniques practices and experiences best current practice of SRE software reliability measurement experience measurement-based analysis of software reliability software fault and failure classification techniques trend analysis in validation and maintenance software reliability and field data analysis software reliability process assessment emerging techniques software reliability prediction metrics software reliability and testing fault-tolerant SRE software reliability using fault trees software reliability process simulation neural networks and software reliability. Appendices: software reliability tools software failure data set repository.

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Handbook of software reliability engineering

A number of analytical models have been proposed during the past 15 years for assessing the reliability of a software system. In this paper we present an overview of the key modeling approaches, provide a critical analysis of the underlying assumptions, and assess the limitations and applicability of these models during the software development cycle. We also propose a step-by-step procedure for fitting a model and illustrate it via an analysis of failure data from a medium-sized real-time command and control software system.

Software Reliability Models: Assumptions, Limitations, and Applicability

This paper investigates a stochastic model for a software error detection process in which the growth curve of the number of detected software errors for the observed data is S-shaped. The software error detection model is a nonhomogeneous Poisson process where the mean-value function has an S-shaped growth curve. The model is applied to actual software error data. Statistical inference on the unknown parameters is discussed. The model fits the observed data better than other models.

S-Shaped Reliability Growth Modeling for Software Error Detection

The discrete Weibull distribution is defined to correspond with the Weibull distribution in continuous time. A few properties of the discrete Weibull distribution are discussed.

The Discrete Weibull Distribution

The s-shaped growth curves of detected software errors can be observed in software testing. The delayed s-shaped and inflection s-shaped software reliability growth models based on a nonhomogeneous Poisson process are discussed. The software reliability growth types of the models are investigated in terms of the error detection rate per error. In addition, a viable method for the software quality assessment, which integrates the capture-recapture method and the models above, is discussed, and its application to actual test data is illustrated.

s-Shaped Software Reliability Growth Models and Their Applications

Software reliability measurement during the testing phase is essential for examining the degree of quality or reliability of a developed software system. A software-reliability growth model incorporating the amount of test effort expended during the software testing phase is developed. The time-dependent behavior of test-effort expenditures is described by a Weibull curve. Assuming that the error detection rate to the amount of test effort spent during the testing phase is proportional to the current error content, the model is formulated by a nonhomogeneous Poission process. Using the model, the method of data analysis for software reliability measurement is developed. This model is applied to the prediction of additional test-effort expenditures to achieve the objective number of errors detected by software testing, and the determination of the optimum time to stop software testing for release. >

Software-reliability growth with a Weibull test-effort: a model and application

In general it is considered to be unrealistic in software reliability modelling to assume that the faults detected by software testing are perfectly removed without introducing new faults. In this paper we propose two software reliability assessment models with imperfect debugging by assuming that new faults are sometimes introduced when the faults originally latent in a software system are corrected and removed during the testing phase. It is assumed that the fault detection rate is proportional to the sum of the numbers of faults remaining originally in the system and faults introduced by imperfect debugging. These two models are described by a nonhomogeneous Poisson process. Several quantitative measures for reliability assessment are derived, and the maximum likelihood estimations of unknown model parameters are presented. Finally, numerical examples of software reliability analysis based on these two models are shown.

Shunji Osaki

Papers

S-Shaped Reliability Growth Modeling for Software Error Detection

The Discrete Weibull Distribution

s-Shaped Software Reliability Growth Models and Their Applications

Software-reliability growth with a Weibull test-effort: a model and application

Imperfect debugging models with fault introduction rate for software reliability assessment